If you are someone who's looking to get into AI product management, then this is a must-watch video for you. Few days ago, I conducted a free master class for above 500 people who joined this session and then they were able to understand how AI product management is done. Now I've decided to make this video available for everyone so that everyone can take the benefit. My only request is that before you go ahead and start watching this video, keep a notebook and pen with you so that you are able to take notes and have your full attention in the session. I'm sure this is going to help you out and if it helps, go ahead and let me know in the comments. Thank you and happy learning.

So, we have divided this two-day master class into three components. The first component is Gen AI fundamentals where we'll talk about the Gen AI value, tech types of AIPM, and nature of large language models. Then we will talk about inside EIPM. We'll talk about context engineering, RAG, prompt engineering, fine-tuning, AI agents, and AI events. Even if you're not able to understand anything right now, it is my promise that by the end of tomorrow, you should be able to understand these topics. And then we are going to talk about giving you actual examples how products such as Chat, PR, Granular, Gamma, AI Notebook, LM are actually built. Right? So this is going to be the session agenda for these two days. And the motivation or the purpose of this session is not to make you an expert in AI product management because that cannot happen in 4 hours. But the motivation of this session is to make sure that you feel motivated after completing these sessions that yes, I have a clear roadmap. If I can learn this much, I can also go ahead and learn and explore the entirety of AI product management. So this is to give you the confidence so that you are able to get started. Right? And tomorrow and today and in today's session, I'm going to ask you in the end that whether you are feeling motivated after the session or not. Right?

Now let's talk about the Gen AI revolution. Right? The companies that you are seeing on the screen, they are now among the world's most valuable companies and many of these companies have been able to get this kind of amazing transition just after 2022. So from 2022 to 2023, this company OpenAI has got a valuation of 500 billion. This Anthropic company, which was not existing a few years ago, now has a valuation of $183 billion and then we have something called as XAI Elon Musk, which has the valuation of $200 billion. Not only the new companies are doing well, but the older companies which are betting on AI as a revolution, which is Microsoft, has gone from so Microsoft was founded in, yes, Microsoft was founded in about the 1980s, 1983, and from then to 2021, they were able to get a market cap of 1.8 trillion, but in the last 3 years, they have grown from 1.8 to 3.8, almost twice increase. Similarly, Google, after betting on AI, they have grown their valuation almost 2.5x. Similarly, Meta has grown too much from 0.3 billion or 300 billion to 1.8 trillion. And then Nvidia, which is the most, which is the company which has most benefited from this AI revolution, it has gone almost 10x of their valuation. Right? And then we have also seen some very new-age companies which are actually created in this period only. So Kua, the company did not exist a few years ago, is now worth $9 billion. Glean is worth $7 billion. 11 Labs is worth $6 billion. And Perplexity is worth $15 billion. So now there is a sharp change that has happened in the market recently because of which people are able to understand that there is this new technology called GenAI which can go ahead and do so many things together. Okay.

But understand, as many people have predicted, this can well be a bubble. So how do you check if something is a bubble or not? It might be possible that all of these companies are overvalued in some of the other way. But let me ask you a very honest question. Okay, everyone, go ahead and answer me in the chat. When was the last time that you have used ChatGPT and Gemini? Yeah, I'm sure for many of you, your one of the tabs is ChatGPT right now. Yes. Cool. Right. So, understand there have been many technologies that have come and gone. For example, there was a, there was a revolution on blockchain, there was a revolution on cryptocurrency, there was a revolution on NFTs. Although you were listening about all of these things on news, on multiple websites, and Twitter, but they could not become a part of your daily life. Right? So the verdict is that it might be possible that these things that we are talking about, the valuation and all these, could be larger-than-life valuations. That is completely possible. I'm not disagreeing with that. But we also have to understand that GenAI is a technology that has been able to prove itself that it is actually very helpful. Because technology, if it is only increasing the valuation, that might mean that yes, it could be a bubble. But if it is generally helping you in your daily life, then it just means that it is just a matter of time that we are able to find its relevant use cases and utilize it for work and more of our personal life. Right?

But how has it happened? Like, what is there in the GenAI revolution? So understand, the GenAI revolution has happened because of an innovation in technology which is called as Large Language Model. Okay. So what is a Large Language Model? A Large Language Model? A Large Language Model is a neural network trained on massive amounts of text to learn patterns in language, enabling it to understand, generate, and reason with natural language by predicting the next, more likely, most likely word or token in the context. Right? This is the Wikipedia definition of Large Language Models. But what exactly is a Large Language Model? Let me go ahead and tell you. Please, everyone, focus on the session. Super important part. Yes. So we are talking about Large Language Model. So a Large Language Model is, it is kind of a code or an algorithm. It is kind of a code or algorithm or a piece of technology that is very good at predicting the next word in the sequence. That is, it gets very good in predicting the next word of the sequence. Right? For example, I just take this sentence. I will take this sentence. I will increase the size. Right? So let's say if I enter this particular thing to a Large Language Model, if I tell a Large Language Model that "A Large Language Model is a", then it is going to return me "neural". It is going to return to me the next word. If I give it a complete sentence that "A Large Language Model is a neural", then it is going to return me "network", and then I will keep on iterating so that I'm able to get the complete sentence. So what is a Large Language Model? Large Language Model is the next word or token predictor. And when you do this repeated number of times, you get the complete sentence, the complete book, the complete essay, the complete summary. Right? So this is the basic fundamental how a Large Model works. You give it some sentence and complete sentences. It will try to complete it. And how this, how does it complete it? Let me go ahead and tell you. Okay.

So understand, first of all, if you guys are able to observe, I have written "token" in the bracket. So what happens is whenever you give a word to a Large Language Model. Okay. So let's say I give a word to a language model that "He lives near the river bank. He lives near the river bank." Okay. So Large Language Model is a machine. It's an algorithm. It's a code. It will not be able to understand what are these exact words. In order to make sure that the machine is able to understand everything, what we do is we convert this into numbers. So in order to make sure that the machine is able to understand things efficiently, we convert these into numbers. These numbers are called as tokens. Let me show you how it works. So I'll go to a website called as tokenizer where if I write this sentence, the sentence is "He lives near the river bank." Right? Now the Large Language Model has converted this. The Large Language Model has converted this into these tokens. So these are the tokens. Right? Every model can generate different kinds of tokens. Every model has different kinds of combination of tokens. Okay. The tokens are generated to make sure that the Large Language Model is able to efficiently utilize its memory and context because numbers are easier to interpret for machines rather than the words. Right? And in total, in English language, we have around 26 alphabets and we would have so many, maybe millions of applications, millions of spellings, right? Vocabulary. In GPT-4, we have 100k tokens. Right? So what is a token? Token is a numerical representation of your text. Token is a numerical representation of your text. Right. And at a high level, three words in English is equal to, sorry, at a high level, in English, two words are equal to three tokens. At a high level, like, not this is not completely true in all the cases, but for most of the times, on an average, two words is equal to three tokens. Why tokens are important? To understand, if you go to any of these applications, let's say, let's understand OpenAI pricing. So if you look at pricing of any of these models, you'll be able to understand that they work on tokens. How many tokens are input? How many tokens are generated? How many tokens are cached? Right? So for every 1 million tokens that you give to ChatGPT, it is going to ask you for the input. It is going to take $1.25, $25 from you. Right? So what is a token? Token is a conversion of your words into mathematical numbers so that the model is able to understand them very, very efficiently. Right? How many tokens are possible in a model is called as the vocabulary size of the model. How many tokens are possible in a model is called as the vocabulary size of the model. Right? So this is, this is called as the tokenization. Right?

Now what happens? The purpose of an LLM is that if you give it a set of tokens, let's say 1, 2, 3, 4, 5, it will try to predict what is the next token. Then if you run it again, then this becomes your full sentence up to six, then it will generate seven, and then you have a new sentence 1 to 7, and then it will generate eight. So it is going to go ahead and complete the sequence. Right? That is at the most fundamental level how Large Language Models work. Right? Everyone, a quick yes or no if you're able to understand everything so far. A quick yes or no before I could go ahead. Wonderful. Great.

Now let me go ahead. But this is a simple explanation. But how does it happen? How does it predict? Okay. So what happens is this is how a Large Language Model works. Okay. So there are three stages. Whenever you, we create, whenever we create a Large Language Model, we use these three stages. Every Large Language Model is created by going through these three stages. The first stage is called as a pre-training stage. The second stage is called as the training stage. And the third training is called as a post-training stage. Right? So in the pre-training state, understand what happens is the models are able to predict the next token very accurately because they are very good with pattern recognition. So what we do is we need to train this model on a lot of data. We need to train these models on a lot of data. Okay. But before that, let me tell you, let me help you understand why this data is necessary. Okay. So how many of you can tell me what is this? Tell me in the chat, what is this, guys? Go ahead and tell me in the chat, what is this? Wonderful. That means you guys have studied in the, in the elementary mathematical classes, right? So y = mx is nothing. It is a very simple equation. It is the equation of a line. Any straight line. This is y-axis. This is x-axis. This is the slope and maybe this is the intercept. Right? So this is a simple equation that is y = mx + c. Right? Now, if I want to know what is the value of y at this value of x. If I want to know what is the value of y at this value of x. So x is the input and y is the output. So now, if I want to know what is the value of x at this, what is the point of y at this value of x? Because there are two things right now, I might not be able to understand because there are two parameters which are unknown to me. I will not be able to understand. But what if I already know two values of input and two values of output? So let's say somehow I was able to find that if x were one, if x is equal to 1, then y = 2, and when x = 0, then y = 1. Right? So let's say somehow it is given to me that this is true. This is called as training data. Training data is something where the output and the input is known to me. Right? Right now, what I'll do is I will try to find out the value of m and c. So y = mx + c. I'll put the value of x and I'll put the value of y. So 2 is equal to 1 into m + c. 2 = m + c. So c = 2 - m. Right? I also have the second value. So what I'll do is y = mx + c. That means 1 is equal to m into 0 + c. c = 1. Right? Now c = 1 and c = 2 - m, which means m = 2 - c. 2 - 1 is equal to 1. So c=1 and m=1. Now the equation becomes, I have found all the parameters. I was able to find the value of m. I was able to find the value of c. So c is 1 and m is also one. So the equation is Y = X + 1. Now you give me any kind of input in the world. Give me any kind of output in the world. Give me any kind of input in the world and I'll be able to tell you what is going to be the output. Right? You give me any kind of input in the world and I'll be able to give you the output. Right?

Now, if you go ahead and amplify the same thing, if you go ahead and amplify the same concept to a billion number of times. So now what happens is a Large Language Model is an equation like this, but it contains almost greater than a billion parameters. Right? So in order to find two parameters, in order to find two values of parameters m and c, I needed to have two values of input and output, also called as training data. Training data that I already know. Right? In the case of a Large Language Model, a Large Language Model is nothing. It is a very complicated mathematical equation, plus the equation contains a lot of parameters. How many parameters? Maybe billions of parameters. Right? So in order to determine these parameters, what I need to do? I need to have billions of training data, which is I need to have large training data, right? And then this is repeated multiple number of times to get the values of these parameters. So how do we get this training data? We get this training data from the pre-training. So before we train any model, what we do is we crawl the internet. We look at the forums. We take the books that are written already, literature, well-written books. And then we convert this data, we clean this data, and then we train on this data. How do we train? How do we train on this data? Understand. Any piece of text that you have, that piece of text can be converted into two parts: the input and the output. For example, let's say in the training data, I was able to get this. So I have written a blog around AI ML product management. Okay. So let's say I have this sentence. So I will collect, I will collect a lot of data. I will break down this data into small parts so that I'm able to feed it to the Large Language Model. And then what I'll do is, let's say one of the training data is this. One of the training data is this. So what I'll do is, I know that this sentence is correct. So what I'll do is, I will just feed this part. I will feed this part as the input and this part as the output. Because what does a Large Level Model do? If you go ahead and give it, if you go ahead and give it any sentence, it will try to complete it. So I'll give multiple billions of rows and billions of, let's say, components of data in the form of input and output. And when the Large Language Model will learn, it will learn from these patterns and then it will be able to give you almost real kind of reasoned answers. Right? A quick yes or no everyone if you're able to understand this. I have tried to help you understand Large Language Models with the help of a very simplistic linear equation. Yes. Cool.

Now let's go ahead. Yes. Cool. Now we have understood this. Now let me go ahead and take you one step ahead. Okay. So now everyone, please look at this very carefully. So how does a model train itself? What happens is this is what the structure is. You have the training data. In the training data, let's say you have this sentence. You are going to break this sentence into two parts. One part is this, the initial part of the sentence. Other part is this, the later part of the sentence. The initial part of the sentence is the input and the later part of the sentence is the output. So ideally, what you want is, you want the model, you want the model to behave in a way that whenever you write "how LLMs", it should complete with "work". Okay. This is what you want. So what you'll do is, you will break the sentence. This is one sentence. This other sentence, you are going to tokenize it. Tokenize as in, you are going to convert it into maybe some kind of numbers. Okay. And every model that you are running, they would have their own method. So there are some free and available APIs available through which you can go ahead and tokenize your content into any kind of numbers. Okay. So we have done the tokenization. Now, whatever are the input tokens, tokens for "how LLMs", are going to be fed as an input. Right? And the output token is going to be here. I'll tell you how it is going to work. Right? So now it will be fed into a Large Language Model. Okay. A Large Language Model is a specific kind of, I would say, right now we are talking about a Large Language Model which is a neural network, and the type of the neural network is transformer. I'll talk about what a transformer is in brief in tomorrow's session. Okay. So a transformer, what is a transformer? It is nothing but a mathematical expression, a non-mathematical expression. Understand it, thousand times more complicated than the line equation that I have given you. And it contains the parameters, the constants that I have told you. Right? And the purpose of this Large Language Model training is to identify the value of these parameters, to identify the value of m, c, and other parameters that it has. It can have billions of parameters. Right? Now, what will happen? It will initially, when it is not trained, it will try to generate anything random. Okay? And also understand one thing that the Large Language Models are not deterministic. Listen to this very carefully. This is super important to understand the language of Large Language Models. I told you that they are next token predictors. Predictors means they will not tell you that this is a token after this. They will give you some probabilities. So "He is a" is the input, and the Large Language Model is going to give you probabilistic outputs. So it is going to give you many options. It will tell you "He is a man", "He is a boy", "He's a father", "He's a singer". So there are multiple probabilities, right? And then he's, it is also going to tell you the, language model, that what is the probability. So let's say "man", the probability is 0.5. "boy", the probability 0.4. "father", probability is 0.05. "singer", probability is 0.05. Right? So it will not tell you the one answer. It will not just give you one answer. It will give you the probabilities of what other words could be there. And then you can go ahead and choose the top word or second word, whatever your Large Language Model can go ahead and pick up. Okay. So always remember, the Large Language Models are not deterministic. They are stochastic, which means they can go ahead and pick random words which are going to be correct in some context. But if you go ahead and give the same prompt to the same model at the same time, it can go ahead and give you different kinds of outputs. Right? So this is an important nature of an LLM to know, and this is also going to be the foundation of when we go ahead and learn about AI evaluations. Right? And how does it generate this? It generates on the basis of training. So it would have seen a lot of data, and based on that data, it generates that yes, I think this should be the probability based on what I have learned. Right? Yes. Cool.

Now going back to a diagram. So what will happen here is, what we have done so far is we have done the tokenization. We have taken the input part. We have fed it into the transformer. The transformer has generated some random results. And then what will happen? We have the real actual token. The actual token is going to be the meaning of "work", like the, the, the numerical token for "work". Okay. And here it is going to generate something. Now it will be compared that "work" should have a probability of one, or "work" should be at the top of the predicted numbers or the predictive tokens. And then we are going to calculate the difference that it should be "word", it should be "work", but it is not "work". So it is going to share this feedback with the transformer. And then what transformer will do is, transformer will correct the parameters inside. And then this model will run on multiple training sets for billions and trillions of times. And then you are going to go ahead and get a model that is going to accurately predict what should be the next probable word. Right? So what we do is we give it the training model. We have the output. We have the input. Then we check. We check the difference between the real output and the expected output. Right? Then we calculate the difference, which is called as a loss function, which is called as a mathematical loss function. This is passed as a feedback, feedback to the Large Language Model. And then when it keeps on doing this billion number of times, it is able to set a very accurate value of these parameters. Right? If you run it a number of times, you will have a pretty accurate model, or a Large Language Model. Right? Understand. If the number of parameters are in a race of billions, this is called as a Large Language Model. If it is less than billions, maybe in the ratio of millions, it will be called as a small language model. Example of a Large Language Model. Example of a Large Language Model is Gemini, ChatGPT. Example of a small language model is Google's Gemma. Right? Yes. Quickly everyone, a quick yes or no in the chat if you're able to understand this, at least at a high level. Wonderful. Great. You guys are intelligent. Great. Amazing. So loss function understands the difference between what is the predicted token and what is the actual token, and then it passes on this feedback to the transformer. The transformer adjusts everything accordingly. Right?

Now, a bigger question that is that should arise in your mind is that why all of these things are happening after 2021? Like, why all of these things are happening very recently, why not before? Okay. So understanding language, understanding language is not an easy task for machines. Why? Because of this reasons. So let's say my sentence is "He lives near the river bank." Right? Initially, traditionally, what people used to do was, if they want to create any kind of AI, if they want to create any kind of predictive model or intelligent model, what they will do is they will work on word by word. You might have seen autocorrect in your mobile phones, right? Whenever you try to write something, it will only catch the last word and then based on it, it is going to predict. It will not look at the entire sentence. Okay. But people who are able to understand, people who understand NLP or Natural Language Processing or programming, they were able to understand that it's not enough. They were able to understand that if, if we want to really understand language, we need to understand the meaning of each word with respect to that sentence. For example, in this particular sentence, I have written "He lives near the river bank." So in this, "bank" means the side of the river. Right? But if I did not know this sentence, I could have as well concluded that this "bank" is a financial institution. So in 2017, a group of computer researchers created a research paper, published a research paper. They all were working in the Google Brain and DeepMind team. And the name of the paper was "Attention Is All You Need". And they told, they came up with the hypothesis that if we really want to truly understand the language models, if we truly want to understand the language, rather than looking at things token by token, we should try to find out what are the relationships of these words with the entirety of the sentence. Initially, these, these predictive models can only look at one word at a time. But now, with this approach, we were able to go ahead and consume all the sentences and essays and whatever is the context window at once. So every word is now able to map its relationship with other words. So these words are created into tokens and vectors, and then the relationship between all the vectors is calculated real-time, and then as a whole, we are able to understand what does this paragraph, what does this sentence, what does this context says. That was a very big shift of how GenAI was born. Okay.

But now, because of that, one problem occurred. The problem was the sequential processing of data was easy. It can be done on a CPU. But if you need to process multiple sentences, multiple parts of the sentences at once, understand when I'm talking about the sentence "He lives near the river bank" at once, the algorithm or the computer has to calculate this relationship of "he" with "lives" with "near" with "the" with "river" with "bank". Then parallelly, it needs to understand the relationship of "lives" with "he", "near", "the", "river", "bank". And then it also needs to understand the relationship of this with this. So now, understand this is complicated math. This is resource-intensive math. Resource-intensive math. It will take time. It will take effort. It will take a lot of processing power, right? So in order to create a good, solid Large Language Model, you need two things. First, you need a lot of data on which you can train so that AI is able to find patterns. Plus, you need super huge processing power. And now people started thinking, where do we get this processing power from? Because quantum computers are not yet invented, at least at the scale for practical purposes, and CPUs are very much limited. Then people thought that these kinds of matrix multiplication, like one-to-many multiplication, also happen at GPUs. And GPUs are running on GPUs. So people understood that yes, transformer is an architecture. So the neural networks or the algorithms that operate in this way by calculating the, the relationship of one word with every other word. These kinds of algorithms are called as transformers. They work on the mechanism of attention, which is one word is paying attention to every other word in the sentence. Attention means one word is paying attention to every other word in the sentence to derive its meaning. Right? And then the neural networks or the algorithms that use attention as a mechanism to predict are called as transformers. Right? And transformers are best run on the same technology that runs these complicated games, which is GPUs. Right? So this is a history about how GPUs were best suited for running Large Language Models. And that is also the reason why we were able to get a huge jump here. We were able to get a huge jump here. Right? Everyone, a quick yes or no if you're able to understand this. Yes. If I see one more LinkedIn link in the chat, I'm going to block you. Don't distract other guys, please. That's so, Large Language Models run on GPUs. They are trained on GPUs. Right?

Now, so we have understood this. Now let's go ahead. Okay. Now the third part, I have told you about the two parts already. The third interesting part of a Large Language Model. So we have talked about pre-training, we have talked about pre-training, we have talked about training. Now the third part is post-training. Now after you generate, after you train a Large Language Model on all the internet data that is available, you get a personality like a baby. A baby has the ability to think. It has the ability to perceive its surroundings, and they will also be able to go ahead and maybe blabber a few words, right? But they don't have a personality yet. So depending upon a baby is born in a Hindu family or a Muslim family or a particular culture, they are going to get their nature. So if you are reading Quran, you are going to think this is the God. If you are going to read maybe Gita, you are going to understand that maybe this is the God. Okay? Reality could be something else altogether. But based on how you are conditioned, based on how you are trained after you are in the world, that is called as post-training. That gives you a personality. So post-training is very important. It converts the next token predictor machine, next token predictor machine, to a helpful assistant. It converts the next token predictor machine to a helpful assistant. Right? So what we do is we hire real human laborers, like what, what do we call it? People who go ahead and label the data. So these people give accurate information to the Large Language Models, and then this data is fed again into the training, and then the Large Language Model gets a personality. For example, there is one thing that Large Language Models have struggled for a long period of time. Okay. So Large Language Models generally tend to hallucinate. Although the hallucination has reduced very, very recently, but still there is, let's say, a lot of hallucination still. Okay. So Meta came up with this very interesting perspective. What they did was, they just gave some sample input and output to the Large Language Models. In the input, they told some random question, like a question for which the answer is not possible. Right? For example, they created some random names and asked GPT or asked, let's say, Llama, that who is this person? Initially, before this training, Llama or ChatGPT, they are going to give you random answers. Even if the person does not exist, they will try to frame something. That is called a hallucination. They are going to give you wrong answers, but they'll give you with complete confidence. Right? So what Meta did was, it gave them some questions which did not have any answer, and in the output, they mentioned, they asked the Llama model to give the answer as "I do not know about this." "I do not know about this." And then once the Large Language Model started understanding that yes, I can give the answer that I do not know about this, that is okay. Right? To show you some other sample data. Okay. So this is how the data looks like. For example, we want to understand about summarization. We want to make sure that our model is able to summarize. So we give this kind of input. "Summarize the following passage about neural network in one paragraph. A neural network is composed of layers of neurons that transforms inputs." Okay. So we'll give this prompt, and then this is the desired output. This will also be fed into the Large Language Model, and then we will be able to retrain it again and again on this data to make sure that we are able to get the right answer. Right? We are able to go ahead and get the right answer. Okay. And then these are the methods in which we are going to test. It has to be clear, factual, summary, and neutral. Right? So we give all of these feeds to the Large Language Model after it is created to the base model, and then we are able to get a more intelligent model. Right?

Now guys, all of this data, all of these files, all of these presentations, the notes, and everything would be given to you if you remain till the end of the session today, and then we are going to go ahead and, and you go ahead and give the submit the feedback form. Okay. Yes. Cool. So I hope everyone was able to understand how Large Language Models works. Everyone, please tell me on a scale of 1 to 5 if you're able to understand whatever I have told you so far. On a scale of 1 to 5, please. One, you did not understand. Five, you understood it well. Cool. We have 821 people. Amazing. Great. Super. Super guys. Amazing. Amazing. Thank you. Yes. People who are not able to understand, just look at the recording. I'm sure you'll be able to go ahead and understand this well. Yes. Cool. People are not able to understand, please focus better. Please focus better in the session. Yes. Cool. Let me just go ahead. Yes.

So now, based on whatever I have told you, now let us go back to economics. Okay. So we have talked about mathematics, we have talked about computer science, we have talked about psychology of a model. Now let's go back to economics and the business. Okay. So as a product manager, you need to understand at what levels is the value created in the Large Language Models by the Large Language Models. Okay. So the first layer is the infrastructure layer. This is the layer on top of which Large Language Models operate. Okay. What they do is, here we have companies such as Nvidia and Google's Vertex AI, which provides the tensors or the GPUs where these models can run. So they provide computation, data platform, GPUs, and all. Right? So the core technology that you need, which is infrastructure, which is, uh, how, where do you deploy these models, is provided by these infrastructure layer companies. The second layer is the model layer companies who are creating and owning these models. Companies such as OpenAI, Anthropic, Meta, Grok, uh, and DeepSeek. These are the companies which are owning these models, and they will give you Large Language Models, small language models. They will also give you fine-tuning. What is fine-tuning? We are going to discuss in the coming sessions. Right now, this is the core. Now, on top of this, there is a bigger opportunity that you, as a product manager, can utilize all of these technologies, like these models, in order to build some amazing use cases. So Lovable, Gamma, Chat, GPT, Gemini, they are all based on this model and the infra layer. So you, as a product manager, can play a very important role in the application. Application layer is how are you able to utilize the power of a Large Language Model in order to solve the problems for your customers. Right? So internet is provided by someone else. The routers are provided by someone else. But Amazon has created an application on top of the internet, which is able to give you e-commerce at your doorstep. Tinder has created an application on the top of the internet so that you are able to find your next date. Similarly, in AI, you should be able to create a lot of applications on these, on the top of these Large Language Models, so that you are able to create a lot of value. Right? And the fourth layer is, you can use these tools, you can use these Large Language Model-based tools such as ChatGPT, such as Gemini, such as Coursera, in order to make your clients and make yourself more productive. So service agency companies such as TCS, Realast, and all, they will use these tools, and then they will help the company achieve their outcomes. Right? So this is the whole value chain. People create infra, then some people create models, some people create applications based on models, and some people create services. Okay. But this is where you can drive a lot of value without investing a lot of money. And this is value at scale. Right? All of the companies here, all of these companies are same companies. They don't own models. What they do is they work on top of models created by other companies, right? And they are able to generate a lot of value. Right?

Now, based on this GenAI value stack, we can divide multiple kinds of AIPM jobs. Okay? So, first of all, understand that every PM is an AIPM. If you are using any kind of AI technology in your life, GPT, Gemini, or anything, you are an AIPM. So I divide AIPM into two parts. The first part is the AI-enabled PMs. These are people who are using products like ChatGPT, Lovable, Jira, or any other AI product in order to make themself more productive, in order to solve problems for their customers, in order to save time, in order to provide more impact. So anyone, any PM who is a user of any of these AI tools, they are an AI-enabled team. So 100% of all of us are AI-enabled PMs. Right. The second is AI Product PM. Now, this is where the value is. This is what we are going to talk about, which we have talked in the last session, in the, uh, today's session as well, initially, and then we are going to talk about today more and in the next session as well. Okay. So AI Product PMs can be divided into two parts. One is the Core AIPM. These Core AIPMs need to understand how the technology behind AI works. So training, pre-training, post-training, how do you create a Large Language Model? How do you store? How do you make it efficient? How do you consider memory of a Large Language Model? All of these core things are the responsibility of a Core AIPM. So people who are creating the infra or the model are the Core AIPMs. These people need to be technical and they should have a solid understanding of machine learning in order to become a Core AIPM. Whatever I have told you so far about how GPT works and all this is a work of a Core AIPM. The second kind of PMs, where most of you should be going, is Applied AIPM. Now, Applied AIPM are the people who are creating useful applications by using these core technologies. So people who are building Notion AI, Grammarly, Lovable, Chat, GPT, people who are building these products are Applied AI PMs. Applied AI means the people who are actually using AI tools, like AI technologies such as models and infra, in order to build something helpful for their customers. Okay. So these are the only the users. These are the core technology creators, and these are the product creators. Right? So can you guys tell me where do you fall right now? What is your best description right now? Where do you fall right now? Yes, wonderful. So all of you fall under the AI-enabled part right now, but eventually you should go ahead and, Yeah guys, everyone is trying to focus and learn. Please do not put any kind of links in the chat. I've already blocked a couple of people. Do not let me to do it more. Let us go ahead and focus on the session. Cool.

Now, this is the differentiation. Everyone is here. If you are coming from already a very strong technical background, you should try to move here. But everyone, even if you are coming from a technical or non-technical background, you can always move here. This is where I want you to move. Okay. Now let's go ahead. Okay. So tell me everyone. Tell me everyone if you are able to understand this meme. Look at this very carefully and tell me if you are able to understand this meme. Yes, cool. So understand there are like so much valuation that has been created in all of these things. Okay. This is sitting on the top of LLMs. Okay. So I'm going to give you some examples of how various kind of technology, the various kind of technologies are working on top of these Large Language Models. Okay. But we are right on time. We will take a quick break here. We will take a quick break of five minutes and then we will come back. Okay.

So now let me go ahead and tell you how do. So now you have understood that yes, a Large Language Model. Okay. So there is something called as a Large Language Model, which is provided by companies such as OpenAI, Anthropic, Meta, Google, and many more. Okay. These are the Large Language Models that are provided by so many companies. Now, right now, these Large Language Models can do three things for you. Okay. The first thing is they can understand content. They can understand what you are trying to say because they have seen so many patterns of data. They're able to understand what you are trying to say. Okay. So if you ask it to generate a summary, it will be able to understand the whole paragraph and then it will generate the summary. Okay. The second is it is able to transform content. Transform means if you ask it to create a summary of some content, it will transform that into a summary. If you ask it to create a quiz on some content, it will be able to transform that into code. Okay. And the third part is it is able to generate content. So if you want it to generate images, code, anything, because it is data, it should be able to generate because it has seen enough code in its life. Right? So it will be able to go ahead and generate. So as a Large Language Model, the capabilities of a. So as a product manager, you should understand that there are three core capabilities of a Large Language Model. The first is it can understand. Second is it can transform. And third is it can generate data. Right? So you can use it for multiple ways. You can use it to generate summary. You can use it to generate answers for your questions. You can use it to generate code. And you can generate for almost everything that you want, right? And because they have been trained on tons of data, tons of terabytes of data, generally the accuracies are very accurate. So now what happens? So far, whatever the software that we used to create, so far whatever the software that we were used to create, these softwares were generally of four types. Okay. So any software that we have used so far, what they will do is they will generally create data, they will read data, they will update the data, or they will delete the data. So all the software that we have seen, let's say Facebook. On Facebook, what happens? You go ahead and create a profile, and then on your profile, some database element is created, and then your people can see your profile. Some people can go ahead and upload some videos, and then you are able to see these videos and entertain yourself. So generally, most of the applications before the AI world were actually create, read, update, and delete. This is called as application. But now AI has been able to give a brain to your softwares. So as a product manager, you need to understand what this Large Language Model can do and how can I utilize it to create better products. Right? So I'm going to give you this understanding with the help of few examples. Okay? So there is a product called as Granola. Okay. And this product is similar to many other products. So it's a very simple product that if you go ahead and use Granola, it will go ahead and get added to your calendar. And whenever you are joining the meeting, it will take the transcription and recording of that meeting, and it is going to summarize the meeting for you so that you do not have to pick up a.

notebook every time. What happens is whenever we are joining the meetings, we have to write down that what are the things that are discussed so that we do not forget. And then we create a minutes meeting, like the minutes of the meeting, right? So there are multiple tools like Granola, there is Fireflies, Otter, Fathom, most of you, most of them which you can see also on the uh, the attendee list, right? Because many people have added their note-takers.

So now, how these note-takers are created? What is the value behind them? So I'm going to show you a simple tear down, okay? So, everyone, a quick yes or no in the chat if you understand what an AI note-taker is. Amazing. Great. Yes. Yes.

So now, let us try to understand how this product works. And this is like a very important case. It will help you understand how AI products are built. Okay. So first of all, understand everyone, whenever you create any product as a product manager, you have to look at five angles. Don't just go ahead and just think about technology. Technology is one important part, but it is not the most important part. But AI is just a piece of technology. So do not focus on just using AI. Focus on solving the problem of the user.

So whenever you look at any product, please ask yourself five questions, right? The five questions are: What problem does it solve in people's life? Right? What problem does it solve in people's lives? Okay. So can you guys tell me? Think about it for a moment and then tell me in the chat what do you think what are the problems that Granola solves in people's life? Think about this as a product manager. This is the most important value that you need to add, that how am I being useful to the users? What problem does it solve? So tell me, what is a problem that these note-takers solve? Yes. Very good. So at a task level, it helps you record and summarize your meetings. But that is just an output. The outcome is that it is able to save you time and it will help you be more accurate because when you are writing something, it might be possible that yes, you are missing something. And you also have to invest a lot of time and attention when you are writing things, right? So Granola saves you time, it makes you more efficient and accurate. Always remember, it is okay if you're not using AI in your product. AI is not a silver bullet that will improve your product. But if the product does not have a value, if you are not able to clearly define why this product should exist, then please do not create the product. Okay. This is the most effective value that you add as a product manager.

Okay, now the second part, which is a business value. Why should the business pursue this opportunity? Okay. Think about this for a moment, everyone, and then tell me in the chat why should the business pursue this opportunity? How do you find this out? Let us keep it collaborative so that everyone is able to learn accordingly. Yes.

So now, what we'll do is, now what we will do is we will try to understand that if we are creating a meeting assistant, how many people in the world are first looking for this kind of solution? So you will have a prediction that people who are a part of small businesses or large businesses, they are going to love this kind of solution because they are going to save time. And then you are going to understand how many people are there who are working in these small businesses. How many small businesses are there? And then you are going to size a market opportunity. Okay. That is one way that it is going to help the business because there is a large market opportunity. And in order to do this research, you can go ahead and use ChatGPT, NotebookLM, Gemini, or anything, right? That is the business value.

Now, the other kind of business value is, let's say we are not talking about Granola, we are talking about Zoom, right? So because Zoom already has a lot of users who are using its platform to conduct meetings, it is a strategic opportunity for Zoom. It is a strategic business opportunity for Zoom to create an inbuilt meeting assistant, right? So it aligns with the strategy and it also aligns with the market opportunity, right? So understand everyone, as a product, before being an AI product manager, you are a product manager. And the purpose of a product manager is to bring the user value and to bring the business value, right?

Now, the third thing is, how do we make sure that the product is intuitive for the user to use? How do we make sure that the product is very intuitive, right? So in all of these tools, you would have found that you do not have to do anything. You just install the tool, you add it to your calendar. Automatically, whenever a meeting occurs, the person, the meeting note-taker would be automatically added. Granola goes to the next level that right now, this meeting is recorded with Granola, but you do not know why because it is installed on my computer. It is taking over the microphone and the video and it is doing it by itself. So now you have to do optimizations on the design part so that you are able to reduce the friction for the user. If I have to add a Granola every time, if I have to add uh, uh, this Fireflies every time when I'm doing a meeting, that is going to be resistance for me, right? So this is called as being a proactive software. So they are able to get meeting into meetings automatically. So you don't have to do the work, automatically it is happening, right? So this is the intuitiveness. And then they have created an interface so that I'm able to see what are the meetings that I have conducted. I can go ahead and ask questions based on my meetings, right? So this is the value part, business part, and the design part.

The next part that we are going to understand today is how will this be engineered? Engineered part is feasibility part. But please remember this line: User value and business value, as a product manager, should be your first focus. And feasibility should be the next focus. Feasibility should not be the number one focus of a product manager, right? Now, please start with the user problem and the business problems. That is what you add the most to the company. That is where you are being most impactful, rather than only talking about engineering and feasibility, because there are people who are more eligible than you who can do the task. Okay.

So, how will this AI product be orchestrated? But before that, everyone, a quick yes or no in the chat if you are able to understand the three parts: user value, business value, and design value. Amazing. Wonderful. Yes. So there is one person who has said no four number of times. Great. So yes.

So now we have understood the three parts. Now let me give you a bit about engineering. Okay. So this is how this product looks at the back end. Now look at this very carefully. Super important. If you just understand this one product, I can assure you that you can reverse engineer almost any AI product. So please focus on this slide. Okay.

So behind Granola, whenever you do any meeting on Zoom, on Meet, or anyone, Granola, or Fireflies, or Otter, or any of the meeting recording tools, even the Zoom tool, they will be connecting to the meeting. Either they can join the meeting as a bot, or they will capture the content from your screen. Whatever you are speaking, they will take over your mic, or they are going to ask the API. API as in, they will ask Zoom or Google Meet to share the recording or the transcript if you have given the permission. Okay? So they will get the audio of the meeting, right? Once they get the audio of the meeting, they are going to convert that audio into text transcription. There are multiple APIs available for the same. So OpenAI has an API called as Whisper, where you give it audio, it will be able to give you the transcript of that audio. There is also Google Text to Speech, like uh, Speech to Text, where you can just go ahead and give it an audio, it will be able to convert into accurate transcript, right? There are some smaller players as well, which are Assembly.ai and other players who can also do this job. Okay?

So now, what we have is, we have a transcript. Now, this transcript is available with us. What these people will do is, what Granola or Fireflies will do is, they will take this transcript and then they will add a system prompt. Okay? So this is a system prompt. So now, everyone, please look at this very carefully. Look at this very, very carefully. So it is going to add this prompt: "You are an expert Chief of Staff and note-taker. Given the following raw meeting transcript, generate a structured and actionable summary that includes the following sections: meeting title, data and participants, date and participants, key decisions made, action items, open questions, summary, notable quotes." And then you have written some more instructions, right? So this prompt might be created by a product manager at Granola, which will convert a raw transcript that is generated into very helpful meeting notes, meeting notes, right? And this prompt plus this transcript would then be sent to a large language model, right? This will be sent to a large language model such as Gemini or ChatGPT, right? And then it will do its magic. It will create summary, actionables, and tasks. Okay? So this is how these meeting tools work.

And then what happens? You can take it to the next step. You can make it more agentic. Agentic means they will take action. So what can happen is, they can also go ahead and push these tasks to your Slack, to your calendar, or to your Jira. So now you are just adding a meeting note-taker, like a meeting assistant, to your calendar, and then it will get the recording. It will do the transcript. It will go ahead and create the summary with the help of system prompts and the transcript. Then it can go ahead and convert into multiple tasks. And then it can also, if you give it the permission, it can also go ahead and add it to your Slack, calendar, or Jira, right? So this simplistic architecture, which is dependent on multiple LLMs and things, is now a possibility because large language models are possible, right? Everyone, a quick yes or no if you are able to understand this, right? So I want you guys to think in this manner. Okay. I want you guys to think in this manner. Everyone, a quick yes or no in the chat, please. Yes.

So now, large language models have unlocked intelligence for you. Okay. Now you can use them like an expert in order to go ahead and do a lot of things, right? So now, this is like a simple orchestration. Now you can think about how you can innovate on top of the same, right? So the history of Granola is that before Granola, there were players like Fireflies, Autoi, and Fedum. What happened because of these was they will keep themselves added on. They will keep themselves added on. Yes. They will keep themselves added on multiple meetings, and people generally get irritated if so many bots are joining the meeting. Okay. And it was also not very privacy-incentive, incentive for intensive for other people. Okay. That people do not want your bots to join their meetings. Okay. So Granola came up with an AI with a UI innovation. With Granola, you don't have to add anything. It will operate in your computer. It will listen to the audio and then it will, it is going to automatically transcribe the meeting for you and give you the context, right? So you can do these optimizations in order to make sure that you are able to go ahead and yes, you are able to go ahead and create a lot of things, right?

Now, the magic that you can add as a product manager is, understand AI is not difficult. You just have to understand the concept that I'm telling you. After that, the next real skill that you have to develop is customer empathy. Understanding what customers want and giving them as soon as possible in the best design possible. That is the value that you add as a product manager. Okay.

Now, we have talked about Granola. Now let's go to the next level. Okay. So Granola is a very simple example. Okay. The next example that we need to understand is, okay. Okay. So one part is left. That part is, understand, just building a product which is useful, and business wants to build it, and it is good design, it is good feasibility, doesn't make sure that product is actually very, very helpful, right? Doesn't make sure that the product is very, very helpful. So there is one more thing. Okay. There is one more thing that is needed to make a product achieve its potential and generate revenue out of it for the company, which is, how do we grow this product in this market? Which is creating a go-to-market strategy, building a product that is actually loved by the users, creating distribution for the product, making sure that people are actually using the product. That is called as the growth part. So how do you grow in this market? So all of these meeting assistants, they are viral by default. So let's say I and you did a meeting together, and because I believe in productivity, I do not believe in taking notes manually. I have added a note-taker called as Fireflies. Now, when you see this, that in the meeting, there is another thing which is Fireflies, you are going to get curious that what is this? I should also go ahead and check what is this. Okay? So by using virality, they are going ahead and creating a distribution, right?

Similarly, whenever the meeting has ended, most of these people are going to send you emails. Let me show you one of the emails. Just give me a moment. Let me check if I have those kind of emails. What is the name? Fireflies. Yes. Cool. So, let's say, cool. So, this is the link. Okay? So generally, when we do the meeting, this is the link that we receive after that. Okay? So someone added me to their meeting, and they also added Fireflies because they were using Fireflies. And after the meeting has ended, it always sends me this thing that, "This is what you have done. This is a summary, this is the action item." And it also tells me to go ahead and, if I click on "View the meeting" and all, it will ask me to sign up, right? So these are some growth tactics that these people are implementing in order to make sure that it is able to grow. The product is actually able to succeed. So marketing and growth is also a good responsibility or a must-have responsibility for a product manager, right? And understand guys, product management is multi-dimensional. Not that you just create a great product and put all of your efforts into it, and it is going to work. You need marketing, you need stakeholder management, you need a bit of understanding of technology, you need a bit of understanding of analytics so that you know if you are moving in the right direction. It's a multi-dimensional job, right? So this is the story behind Granola.

Now, let me go ahead and give you some more complicated examples. Okay? So tell me everyone, if you ask ChatGPT, if you ask ChatGPT that, "What is the most important item in my Google Calendar tomorrow?" Would it be able to answer? If you ask ChatGPT that, "What is the most important item in my calendar tomorrow?" Would it be able to answer? Yes, if you have not connected a Google Calendar, it would not be able to answer, right? And if I take the example of, let's say, this particular product called as Calendar is connected to Google, but if I ask about Notion, then Gemini will also not be able to answer, right? So now we have so much data in our calendar, in our to-do list, in our Jira dashboard, in our Stripe document, like Stripe dashboard. But how do we make sure that all of this content is available to large language models so that they can go ahead and give us the answers? So rather than asking like, rather than thinking that, "Where is the PD? What is the task that I need to do today from my Jira or Notion?" I should just ask a query that, "What are the three things that I need to get done today?" And then it should give me the answer. Okay?

If I want to go to this level, what I can do is, a very simple approach that I can do is, I can just go ahead and give all the data from my Notion or Stripe or Atlassian to the large language model. Okay? But when I give a lot of data to the large language model, what happens? The model runs out of context. Okay? Because these models cannot carry infinite context. Context means the kind of input that you can provide to these models, right? So now we have to work, we have to work hard to make sure that these models work. Okay? And also, although companies are trying to increase the context window as much as possible, there has been research that is done by great people at Chroma, which is a vector-based database company. They were able to find that as you increase the context, as you increase the context, the performance of the model goes down. Even if these people claim that our model can work in so much context, when these people actually tested multiple models, they were able to find in almost all the models that as the context or the input size increases, if you increase the size of your prompts, then the quality of performance actually reduces after a certain level, right?

So now, understand as a product manager, so even if someone is not able to understand this or this, at least you'll be able to understand this. Okay? So when you give concise input context to the large language models, they appear to be very intelligent and very insightful and very careful. But when you go ahead and give them too large input, then they don't know what they are doing and they get confused. Okay?

So now, how to solve this? What we do is, we have another methodology called as context engineering. Context engineering means how do you carefully curate what should go as an input to the large language model so that it is accurate, so that you have given the right kind of information, so that you are able to get the right kind of insight or output from the large language model, right? So this is called as context engineering. So context engineering, I am going to quote the person who has acquainted this term, which is Andrej Karpathy. So he says that, "Context engineering is the delicate art and science of filling the context window with just the right information for the next step." Art and science means you have to apply your mind. You have to think creatively to give enough context to the large language model so that it is able to answer you in the most accurate format, right? So this is context engineering. And in context engineering, we do multiple things. Okay?

One of the point of context engineering is prompt engineering. How well you write the prompt is a part of context engineering. Okay? But apart from prompt, what happens is, you need to give some data about your software. For example, if you want to build a software called as Notion AI. Okay? So let's say you want to build a software called as, not Notion AI, but let's say you want to build a Bloomberg chatbot. What is a Bloomberg chatbot? Bloomberg is an organization which has access to a lot of financial data available in the world, right? So now, what you want to do is that whatever the data is available with Bloomberg, you should be able to send a question to a large language model in a natural language, and then it should be able to give you an answer based on the content that is available with Bloomberg, right? So here, the problem is, you cannot store all the content that Bloomberg has in the context window. You cannot have all this content as the input. Why? Because it is going to run out of context.

So what you will do is, you will carefully build something called as a RAG pipeline. Now, this is a very important concept. Even if you remotely want to become an AI product manager or AI practitioner, you need to understand this. This is like a very basic thing that every person who calls themselves AI-enabled has to understand. Okay? So RAG means Retrieval, Augment, and Generate. Retrieval, Augment, and Generate. Okay?

So what happens when someone comes to your house and they tell you that, "I need some sugar." Okay? When they tell you, "I need some sugar." You don't go to your study room and you do not look for sugar in, you do not start by looking for sugar in your cupboard, or in your wardrobe, or in, let's say, your library, or maybe under your sofa. What you do is, you straight away understand that sugar is an item that might be available in my kitchen, in a particular shelf. It might be possible you don't know that which part of shelf that it is put on, but you know a particular shelf, or maybe you at least know that it is going to be found in the kitchen. Similarly, if your kid asks for a book, or if they ask for money, you do not go ahead and go anywhere else in the house. You know where the money is put. You are going to just pick the room where the money is. Then you are going to pick a particular wallet, and then you are going to see in the wallet that, yes, in all of these four to five pockets, there could be a place where I have put the money, right? So at a high level, you know that where this information is. The same analogy can be drawn to RAG.

So in RAG, what we do is, the first part is, we get all the data. We get all the data. For example, we have PDFs, or a better example is, we have Bloomberg data. So Bloomberg data is, they have a lot of files. So we are going to break down files into small, small chunks, maybe in the form of paragraphs. Okay? So we have one chunk, we have another chunk, we have another chunk. We are going to create all of these chunks which are small enough and meaningful enough. Okay? We are going to break down this data. Now, in this data, what we'll do is, what we'll do is, now we have the chunking of data. Now, what we'll do is, we will convert these data chunks into something called as vector embeddings. Now, listen to this very, very carefully. Super important. If you miss this, you will miss RAG. Okay? I'll repeat this again. You will take the data, whatever data is available, you will break it down into small chunks, and then when you have these small chunks, you will create, you will convert these chunks into vector embeddings.

So, what is a vector embedding? So, quick one-on-one on vector embedding, that like large language models, we have also created algorithms which are able to create any form of text into a numerical representation. Numerical representation as in, let's say I have a word called as "cat," right? So I have created a mathematical representation of "cat." Okay? What is a mathematical representation? I have assigned some numbers. Okay? So let's say the number is 1A, 2A, 3A. This is let's say x-axis, y-axis, z-axis, okay? Right? Now, to show you, because we can only look at 3D, I'm showing you three-dimensional, but in reality, it can be n-dimensional, which is n is a very large number, okay? So we take words, we take sentences, then we generate vectors from these sentences. Vector is this, like this point, right? And then what we do is, we keep on generating these vectors. Just a moment. Yes. So we keep on generating these vectors. And the beauty of these vectors is that somehow, when you search for "cat" and when you search for "lion," "lion," okay, because these belong to a same family, they are going to be very closer to each other, right? When you plot something like a "pet," it is going to be much closer. But when you go ahead and plot something like a "table," then it is going to be far from "cat." So mathematically, computer scientists have been able to create this vector thing. So what this vector thing does is, it will take a sentence or a piece of information. It will convert it into a numerical identity, which is a vector. And then these vectors are created in such a way that if you go ahead and compare these vectors with other words, based on the distance, you will understand how much this word is similar to that word or not in the meaning, right? So this is vector embedding. Okay?

So what is happening here? We have had the content and the text. We have converted them into long sentences of vector embedding, which is numerical numbers. Okay? Now, what happens? And then we are going to store these vector embeddings into a vector database, right? So now we have the data ready. We have data ready to be searched. So now, what will happen? Whenever, just a moment, where is that? Yes. So this is RAG. Look at this very carefully, everyone. So there are two parts of RAG. First, we are creating the knowledge base. What we have done is, we have taken all the content, whether it is text, PDF, transcript, code, or any Bloomberg data. We have converted that into small, small chunks, small information of data. And after that, we have created vector embeddings. And then once we have created vector embeddings, understand, there are standard programming code or functions to create these vector embeddings. Okay? Once the vector embeddings are created, they are stored in vector storage. There are multiple databases that give you by default vector storing, which is Pinecone, Supabase, AWS S3, vector storage. It gives you all of that. Okay?

Now, what happens? A user gives a query. The query is, "How has Tesla performed in the last quarter?" Okay? Now, what will happen? This query would be converted into an embedding. It will again be converted into a vector. Now, what will happen? We have two vectors. One is the vectors that are stored. Other is this vector. So if we want to see what are the vectors which are similar to this vector, which is where this information is stored, right? Where is that? Let's say, what, where is that? I would say wardrobe, or where is that place where, where in my kitchen, the sugar might be stored. Okay? So it is going to go ahead and understand this embedding. And then you are going to search for things which are similar to this embedding in this database. Okay? And you are going to pick maybe 1, 2, 3, 5, 10 things based on your algorithm, based on your needs, which are most similar to this particular vector. So you will have things which are most similar to this particular vector. Then what you will do is, you will have some information. The information is, you will go ahead and get this information. And then this will be your final prompt. So now you have taken the vector, you have found what are other similar vectors. Now you have created a list of the vectors which you have found. Now finally, you will get this. Now finally, you will have this. So you will give this prompt to the large language model. Just a moment. Yes, you will give this prompt to the large language model. Please observe this very carefully. "Answer the question using the context below. This is the context." So, chunk one, chunk two, whatever has been retrieved from there, whatever information, okay, that information in the text form, it will be written there. Plus, this is the question from the user, which is the query, which is this, "How has Tesla performed in the last quarter?" Right? And then eventually, you are going to go ahead and give this prompt to the large language model, cloud, Gemini, or anything, and then you are going to go ahead and get your answer. This is RAG in a nutshell. So we first create a vector knowledge base, then we get a query, we match that query for all the similar terms in the vector database, then we get all the information, we create a prompt, then we send it to the LLM, and then we get the answer. So now, with RAG, what happens is, you do not have to store all this information into your model or in the context. You can just cherry-pick the information that is very relevant to that particular information, the prompt, and then you can get all the information at once. Okay? Everyone, a quick yes or no if you are able to understand this. Amazing. Great. Super. Yes, people are not able to understand. Uh, if you just go ahead and, uh, submit the feedback form in the last, like in the next 10 minutes, I will give you the recording so that you can go ahead and do a revision. Cool. Yes. Cool. Uh, guys, tokens and embeddings are different. Embedding is actually a meaningful representation of that particular sentence. Okay? But token is just, let's say, a vocabulary. Okay? It is just a numerical representation of a particular word. Okay?

Now, this is RAG. Now, in order to help you understand better, I have created a small app that I have white-coded very recently. Let me show it to you. Yes. So this is a simple RAG process visualizer, which I created in just 10 minutes using Cursor and ChatGPT. Okay? And in the program that we offer at LOPM, we are going to teach you how to do all of this. Okay? So let me show you what happens in that. So I've told you that in RAG, what happens? You have a piece of information. You first convert it into chunks, right? After that, you create embeddings. After that, you convert it into and save it to vector. And once the vector is created, you are going to write a query, you are going to write a query, and then from that query, you are going to match what are the matching chunks, how many chunks you want to match, and then you will go ahead and compose the query, as in the prompt is going to be composed, and then you will generate the answer. Okay?

So let us see how it works. So I will take this thing. So I have written like a, this log, a very long time ago. So let me go ahead and do this. So I have taken this piece of information. Let's say, till this. I can take any kind of information. I will go to this. I will paste this content. Now, the chunk size is 350, which is, it is going to take 350 characters in a particular chunk, and the overlap of 80, which is, there would be overlap of 80 characters between the first and the second and the third chunk. Okay? So I will just go ahead and create chunk document. Now, look at this. Chunks are created. This is the first chunk. This is the second chunk. This is the third chunk. This is the four chunks. Understand this chunk was removed here. So it is in this detail, 4,000 words guide, setting the context, how to build, then rather than how. Okay? So now, there is overlap in this chunk. So 50 characters are overlapped, so that all the chunks have a meaningful information. So now we have created the chunks. Okay? So what is chunking? Breaking down information into smaller parts so that they are meaningful and smaller enough. Okay? Understand? You cannot use the same chunking method methodology for every application. If you're using RAG for code, in order to retrieve code and in order to generate code, the chunking has to be done mostly at the functional level. If you're creating for a book, then maybe it can be done at a chapter level. If you're doing it for a meeting, it can be done at a maybe, uh, at a paragraph level. So chunking depends upon the product that you are creating. And tomorrow, I'm going to tell you an evaluation, that is, how to decide your chunking strategy. Okay? So this is chunk. Now I will create embeddings. I'll create on embeddings, and then the embeddings are created. Okay? So if I look at this, these are the embeddings, guys. These are the embeddings. Okay? Okay. So for every chunk, I have created vector embeddings. Okay? And this graph that you are able to see, let me zoom it up a bit. So this graph that you are able to see is a representation of the vector. Now I will press the next button to save to vector DB, and then it will be saved locally. Okay? This is not connected to any server. So this is going to be saved locally. And after this, yes. And after this, I will write a query. The query is, "What is AI product management?" Okay? And I want to retrieve three chunks. Retrieve relevant chunks. And then these are the chunks. So these chunks, because there is product management and all, it is being accessed here. So these are the most relevant piece of information that I need to store here. So this is chunk number eight, chunk number two, chunk number three. If I click on this, I'll be able to see the complete chunk. Okay? This is the cosine, which is how distant these things are from each other and from my vector. Okay? And then eventually, what I'll do is, I'll just click on compose query. Now the query is composed. And this is the query: "Answer the following query based on only the provided data. User query: What is data product management? What is AI product management?" And these are these are the chunks, three chunks. Okay? And then when I click on this, this will be sent to a large language model, and then I'll get the answer. But I have not connected the LM because I don't want to pay. And then we have this answer. Okay? But I can always go ahead and put my OpenAI key here, API key, in order to do this. Then the answer would be generated. Okay?

Everyone, tell me on a scale of 1 to 5, if you're able to understand RAG now. Cool. Wonderful. People who are four, I would recommend you to watch the recording once more, and then you'll be able to understand. Yes. Cool. So, this was about RAG. Super important concept, everyone needs to understand. It will also improve your problem-solving capabilities.

Now, the next part is prompt engineering. Okay? So guys, I'll only take 10 more minutes, and then I will share the feedback form. So if you have been here in this session for so long, please remain for another 10 minutes so that we can share the feedback form and you can learn prompt engineering as well. Okay? Yes. Cool.

So now we'll talk about prompting. So understand guys, you need to understand how to give instructions to your model. In order to give the right data, you have understood, you have to use RAG. But in order to give instructions, you need to understand that you need to learn prompt engineering. Prompt engineering, no matter what the world says, it is a super important skill in today's world. Okay? Because you are using, let's say, AI and LLMs for so much of your time in a day, it is worth investing a couple of hours in order to learn prompt engineering. And also, if you are working as a product manager, as an AI product manager, prompt engineering is your way to interact with the LLMs. Okay? So it is worth investing few hours of your time in order to build great products. Okay? So this is also a guide on prompt engineering. So understand, products such as Firefly, Gong, Granola, which are almost a billion-dollar companies, or at least multi-million dollar companies, they are based on these kind of prompts. So you should know how to learn these prompts. And I'm going to go ahead and give you some, I'll also share this guide with you. But right now, let me share with you few tips to improve the prompt. Okay?

So right now, everyone, tell me, out of 1 to 5, how would you rate yourself on your prompt engineering skills? How would you rate yourself on a scale of 1 to 5 on your prompt engineering skills? Three. Don't give yourself three. Three is a very diplomatic answer. Yes. Many modest answers. Yes. Any reflective answers. Okay.

So, first of all, is, first is, please take a step back. So, I'll not give you a very, uh, exhaustive guide that you're not able to even read. I'll give you a very simple guide. And this guide follows the Pareto principle. If you invest, what is like, if you learn these 20% of the skills as a prompt engineer, 80% of your efforts, like problems are going to get solved. The first piece of advice is, please take a step back. Okay? Understand the value of prompts. What happens is, the unfortunate reality is that when you go to ChatGPT or Gemini, when you give it an very awful prompt, it will still be able to give you an answer most of the time because it has given you an answer. Okay? Answer, you go ahead and move forward from there, and then you start working on whatever you are working on. Okay? But understand, always remember, in many phrases, in many places in your life, good can be the enemy of best. Okay? In many places in your life, good can be the enemy of best. Which means you have to understand and recognize what is the value of this task, and is it worth investing a couple of more minutes or five, seven more minutes in order to write this prompt well. Okay? So the first advice that I have for you is, please take a step back and understand if it is worth investing time in that particular prompt, if it is giving you that many results. Okay?

The second part is, write role. Whenever you go ahead and start using an LLM, please try to give it a role. Role means, try to say it, try to tell it that, "Act as a person with 160+ IQ," or "Act as a seasoned product manager," or "Act as someone who is a seasoned psychological therapist." Right? So give these personas to the LLM so that it is able to perform well. If you want to experiment, you can share two prompts with ChatGPT or Gemini or anything. In one, give a very, like, give it a very solid persona. In one, don't give it a persona. You'll be able to observe the answers. Right? So the first piece of advice is, please give it a role. The second piece of advice is, please write the output expectation. Output expectation as in, you want JSON as an output. You want a table. You want to make sure that these mistakes are not repeated. Please write it explicitly. You have to be very, very specific. If you're not specific, it is going to do anything, and then you cannot go ahead and control it. Right?

The third part is, multistep. Multistep means, rather than telling your large language model to give you an answer at once, ask it to think in a step and give you a step-by-step answer. If you know the step, you can tell it the step. If you do not know the step, go ahead and ask it to tell you the step. For example, rather than asking ChatGPT that, "How would I improve this product, LinkedIn?" I would ask it that, "I want to improve LinkedIn. Please follow these steps in order to suggest me the improvements. First, understand who are the different users. Then, understand their important problems. Then, give me what are the, uh, what are the top prioritized three problems. Then, give me the solution of these problems. Then, prioritize these problems and prioritize the solution, and then give me a particular solution." Now, when you are focused, like forcing ChatGPT to work in this manner, you are more probably like to get a better answer. Okay? So please do the thinking. Let it do the thinking in multiple steps. Okay?

After this, give examples. This is also called as few-shot prompting. Few-shot prompting means you are giving it few examples. You are giving it few examples that this is the format that you should follow, or these are the examples that make sure that you are able to go ahead and get the right content. Right? And then we have constraints. So in example, what you can do is, whenever you are writing any piece of blog or any piece of article that you want to learn from, go ahead, copy the best author that you know, copy their blog, and then give it to ChatGPT. For example, what I do is, whenever I write some piece of content, because I have put a lot of efforts in writing this particular guide, I copy this guide and I go to ChatGPT and I ask that, "Go ahead." So let me show it to you. So this is ChatGPT. I am asking ChatGPT to write a detailed article on AI on mistakes that people make while learning AI product management. Right now, we will see the output. Yes. So you can see that it is writing. It is doing like a pretty interesting job, right? In the meanwhile, I'll also open a new chat. Right? So it has written this. You can see that it is, uh, well created. This is point number one. Point number two, point number three, four, this, this, this, this, this, right? I'm still writing, right? This is good one, right? Now, what I'll do is, I will copy this article. I will copy this complete article. Maybe a Ctrl C. Right? So, okay. First, I'll go to this, then I will write. So I've written the similar prompt. Now I'm writing, "Make sure to follow the similar structure, voice, and helpfulness from this example." Okay? And I can go ahead and either I can go ahead and, uh, give it the link. It will be able to crawl the link, or I can just go ahead, to be assured, I can go ahead and give it this content. Okay? So now I have given an example. Let us see how it works. Yes. Now, you look at this. Okay? Rather than straight away jumping to what you should do, what you should not do, rather than straight away jumping to actionable, it is first setting the context. Why do you need this? This is a quick context, and then it is able to give you multiple mistakes and then how you can go ahead and avoid these mistakes, right? This is a slightly better structure than you could have got in the first way. So this is called as few-shot prompting, where you give examples to your large language model as in how they should operate, right? And the other part is the similar example, which is constraints. Please give constraints. Constraint as in, be helpful like this. Okay? Or constraint as in, if you want constraint in terms of, what do we call it? If you want large language model to not do something, then you should always include them as explicit constraints. Okay? For example, sometimes large language models assume a lot. Large language models actually assume a lot. Large language models assume a lot. So you can go ahead and tell them that, "Please do not assume. Only take, check the information that is available in this context that I have shared with you." Similarly, you'll understand more about constraints when we talk about iteration. So understand, prompt engineering is called engineering because it is iterations, which is you tweak your way around. You give it a prompt. You understand that I got the output, but the output does not follow these rules, or the output is not good in this particular way. So you are going to understand the output, and then you are going to give it the constraint that, "Boss, make sure that you are not taking unnecessary assumptions. Make sure that you are not using examples which are, let's say, very much vague or generic. Use specific examples." Okay? So when you go ahead and apply all of these steps, and when you keep on iterating on top of all of these six steps, you are going to learn how to design better products. Okay?

Now, even after I give you all of this, I know that most of the people are lazy and they are not going to follow this. So now I'm going to give you a prompt guide for busy people. Okay? So four things that everyone should learn, and then we are going to wrap up the session. First is, please build a prompt directory. Whenever you come across good prompts, have a Notion document or a Google Doc where you are storing all of these things. Okay? So that you don't have to find your way through good prompts every time, right? Second is, quick shortcuts. Okay? Keep a bookmark where you are able to access this library as soon as possible. So what I do is, whenever I want to write something, I do not want to find a writing pad. What I do is, I just click on this new tab, and then I have

All of my notes from Notion available here. Okay, Chrome New Tab is the name of the extension, and then it is going to load here. Right now, I think Notion is down, so it is not loading, but now it has loaded. Okay, so all of my notes and everything is here. So what I've done is I have set up a Chrome extension where whenever I click on this new tab button, it will automatically load my dashboard, so that I don't have to run here and there. I can just go ahead and click on this, and then I can start writing my notes, and my prompt directory is also there, right?

The other piece of advice is, use a prompt manager. Okay? So there are multiple prompt managers. You can use any one of them. And you can also use this prompt wildcard. Okay. If you are too lazy to write your own prompt, you can also get this wildcard, which is: "Analyze the following prompt idea. Analyze the following prompt idea. Insert prompt here. Rewrite the prompt for clarity and effectiveness. Identify potential improvements or additions. Refine the prompt based on identified improvements. Present the final optimizing prompt." Okay.

And also, there is a prompt creator from Anthropic. Okay. So there is a prompt creator called as an Anthropic prompt creator. Okay, where you can go ahead and create prompts. So it would be somewhere, or I have to log in in order to see this. But you should be able to go ahead and generate this from Cloud Console. Okay. Once you log in, it will give you a prompt creator so that you can go ahead and create your own prompts, which are much better than what you would have gone ahead and written by yourself. Okay.

So this is about the guide to prompt engineering for busy people. Now, in tomorrow's session, what we'll do is we will talk about NotebookLM, how does it work. We will take similar kinds of case studies. We'll detail it enough. Then we'll talk about fine-tuning. Then we'll talk about how to choose between fine-tuning, prompt engineering, and RAG. And then we'll talk about AI agents. Then I'll give you how products such as Lovable, Bolt, Emergent, and Viserio works. And then we are going to go ahead and talk about MCPS. And then eventually, I'll give you some examples of how to write AI evaluations. Okay. This is going to be tomorrow.

But guys, understand, whatever I'm telling you in these two sessions, this is just 10% of what we offer in our bigger program at HelloPM. Okay. So at HelloPM, we have created the world's most detailed AI product management program. Okay. If you want to understand anything about the program, just go to this website called hellopm.co, click on this video, and then I'll explain you what that content is. Okay. This is the most detailed, the most practical, and the most long-term support AI product management program that you can find. The content that I've told you today is just 10% of what we teach in the program, just in the first two classes. We will make sure that tomorrow you'll get a much more power-packed session. So thank you everyone. Bye and take care.

If you have any queries, you can use the same form to publish these queries. Tomorrow, we are going to meet at the same time. Make sure that you also go ahead and bring your friends. Thank you everyone. Bye and take care. You have been a great audience. I hope you got some value from this session and the two hours that you have invested. We'll meet everyone tomorrow. Thank you everyone. Take care.

Today is the day two of AI Product Management Masterclass, and we are going to give you almost everything that you need to get started in your journey as an AI product manager. This is the foundation. Now you can go ahead and explore the whole canvas in order to go into detail. But these are the major building blocks that you need to absolutely know if you really want to call yourself as an AI product management enthusiast, right? And because AI product management is so new, even if you are coming from a very senior background, or you are just aspiring to be a product manager just straight out of college, this content is going to be equally useful for you.

So yesterday, what we have done, we have talked about the agenda. I talked to you about what is the GenAI revolution. A lot of companies are building value on top of this GenAI revolution. Then even in our normal lives, AI has found its way, right? Yes, cool. I will stop the chat for some time so that you guys are not getting distracted. Yes, cool. So AI has also found its way onto our daily lives, and almost every day, we are using ChatGPT or Gemini or some of the other AI tools in order to make ourselves more productive and grow the impact of our work, right?

And then I talked about the most fundamental unit why this AI boom has started, which is because of a particular technology called Large Language Models. These Large Language Models, these Large Language Models are algorithms that are trained on a massive amount of text, and they have learned the patterns of language really well, and they are now acting as the brain of the computers. And now what is happening? They are enabling us to understand, generate, and reason with natural language by predicting the next token most likely, or the token in the context.

And I have told you yesterday how all of these things actually work. I have given you the whole architecture of what an LLM is. The content is like, you will go ahead and take all the content as a training data from the internet. You will break it down into input and output. Then you are going to tokenize it. You are going to send the input from here. The neural network or the transformer is going to predict the next token. And then whatever is the right token from the training data, you are going to compare it. Then whatever is the difference, the feedback is going to be sent to the transformer again. And then it will keep on iterating. When this cycle happens billions and trillions of times, you will have a very mature model, right?

But this model is only a base model. It is trained on some random data from the internet. So it has certain kinds of biases. It hallucinates a lot. So what we do is we go ahead and do something called as post-training. Post-training means we are going to have some human labelers, or we are going to have some data annotators who are going to give some specific input and output, maybe in this particular format. Wait. Yes. Maybe in this particular format, where we are going to insert this kind of data, like well-articulated inputs and well-articulated outputs, so that the human can, the algorithm can go ahead and learn from the same. Okay. This is called supervised learning. And one part of that learning is also called reinforcement learning through human feedback, which is humans are telling how what is correct and what is incorrect, right? Humans are reinforcing the correct things by giving the rewards to the LLM.

Now, after that, we talk about the GenAI value stack. So there are multiple kinds of values. For example, there are companies who are creating infrastructure, such as these big Nvidia and Google. Then there are companies who are creating the models. You know about Anthropic and OpenAI. Then there are companies who are building on top of these Large Language Models. This is where most of the value unlock is. And then we have companies and people who are actually using these AI tools in order to make their clients happy, solve their problems, and also solve their own problems, right?

And then I discussed about types of AI PMs. Everyone who is using AI is an AI-enabled PM. AI tools such as ChatGPT and all. Anyone who is using AI tools is an AI-enabled PM. And then we have other types of PMs, which are more serious PMs. These are PMs who are either working on core technologies such as databases, models, infrastructure, and then there are people who are working on the application layer. And these are people who are actually using these LLMs and models and everything in order to build some truly AI products. Agents are one of the examples. ChatGPT is one of the examples. Grammarly is one of the examples. Lovable is one of the examples. So if you are building these products, not building with these products, you are building these products, then you are an applied AI PM. And the purpose of this session is to help you understand the core AI PM part and the applied AI PM part. Okay. For this part, we have already uploaded a lot of content on our YouTube channel that you can see, right?

Then I talked about how these applications, these meeting assistants such as Granola, works, okay? And not only Granola, but if you take any of the meeting assistants such as Fireflies, Fathom, or anything, everyone works in a similar format. The format is: whenever you want to create a product as a product manager, manager, understand, please do not look only from the engineering or the AI angle. You are the product manager who is responsible to bring the user outcomes and the business outcomes. So please look at these five things. The first thing is, what is the value that is being generated for the users? Why would the users use it? Second is, what is the business value that is generated? Then there is, how do you make design so intuitive that people are able to use it easily? And then how it is feasible with the help of engineering? So all the system architecture is going to come here. And then growth and adoption.

In Granola, we knew that this product helps us save time by recording our meetings and sending us the summary. How does it help a business? The business should invest in Granola, in creating Granola, because there is a large opportunity in meeting apps. So it is going to make money. Similarly, how do we make this intuitive to the users? By making the design very intuitive. How do we make it intuitive? We make the design intuitive by making sure that just after logging and connecting Granola, Fireflies, you don't have to do anything. Whenever a meeting is scheduled, it is automatically going to join that meeting. It is automatically going to generate the summary. It is automatically going to send the summary to everyone who was invited to that meeting, right? With just few clicks, you should be able to do so without your involvement. So that is the intuitiveness.

Similarly, how will this be engineered? So I have told you about this. So let's say Zoom and Google Meet, or any kind of platform is the place where you are conducting this meeting. Then Fireflies or Granola is going to either use the API for Zoom or Google, or they are going to do the audio capture through a bot. Okay? Like you can see that there are a lot of bots in this meeting who are actually recording the meeting on behalf of their administrators, right? After that, that audio is captured, and then there are tools called as audio-to-text transcription machine learning models, right? These models such as Whisper by OpenAI, Speech-to-Text by Google, they are going to convert the audio into text transcription, right? And then you are going to attach a system prompt. So you, as a product manager of Granola, is going to add a system prompt, and then you are going to add a transcript. Let me show you that system prompt that I have shown you yesterday.

So this is the prompt that you will paste. The prompt is very simple: "You are an expert Chief of Staff and note-taker. Given the following things, please go ahead and summarize the summary of the, please go ahead and summarize the given transcript in this particular format." Right? So now what will happen? We have this, we have the system prompt, and then the system prompt is going to be combined with the transcript, and then it will be sent to Large Language Models such as Gemini or Google Gemini or OpenAI's APIs, right? Understand, this is happening at the back end. You do not know what is happening. You are just using Granola. At the back end, Granola is doing all of this, and then it will generate the summary, actionables, and the task. And you can take a next step. You can make it into an agent by actually sending the tasks on a calendar, on your Jira, or on your Slack. So this is how any application which is a note-taking assistant can work, right? Even if you go ahead and understand a bit of coding, you can also go ahead and create your own personal AI meeting assistant. This is the simplest architecture that teams use, right?

And after that, we talk about a very important part that, yes, in this example, we were able to work with prompt engineering, but how do we go ahead and build applications which are able to work on the large set of data, such as data which is on your Jira dashboard, data which is on Notion AI, data which is on Stripe? So what we do is, we cannot, let's say I have a Notion database, I want to ask some questions on top of the database. So I cannot just go ahead and put all of these content in the text, in the context. Why? Because the LLM is going to go out of context, and then there are researches that are conducted which have proven that with increasing context, the accuracy of the LLM decreases. With increasing context, the accuracy of the LLM decreases.

And after this, we talked about the simple thing that if you have, if you are working with LLMs, try to make your inputs concise, otherwise they can go ahead and hallucinate too much, and they are going to run out of context and give you irrelevant answers. And then we presented you to context engineering. In context engineering, we first talk about RAG. So what is RAG? Super simple. What we do is, rather than putting all the documents, let's say I want to build a functionality where on my Notion document or on my Google Drive, I ask a question that, "What is the main, what are the tasks that I need to do tomorrow?" Okay? And somewhere in one of the PRDs, one of the documents, it is written that I have to do these tasks tomorrow, okay? So in a non-ideal scenario, what will happen? I will try to get all the content of the Google Drive, I will put it in the LLM context of the LLM, and then I'll ask the question, "Among all of this content, what is something that I need to do tomorrow?" Okay? And then what will happen? Because it is a lot of content, the LLM, the LLM is going to run out of context.

So what we'll do is, rather than going ahead and taking all the data at once to the LLM, we are going to only find the content that is relevant, right? So what we do in the, what we do here? We simply go ahead. We simply go ahead. First, whatever data that we have, we break that data. We break that data into chunks. And after chunking the data into small enough and meaningful chunks, we are going to convert it into vector embeddings. And after we have converted into vector embeddings, we are going to store in a vector storage. Once the vectors are stored, we will go ahead and get the user prompt, find all the relevant content from the vector storage, then convert it into text, and then ask this question to the LLM that, "Based on these information, please try to answer this question." And then it will give you an answer. Answer the question from the limited context that you have given, right? And I have also shown you a visual example with a RAG model that we have created, like a RAG vibe app that we have created, right? So this is about RAG.

And then we talked about prompt engineering. Okay, I have shared with you some very simple but very effective tips, which is, try to understand the value of prompt engineering. Make sure that you are giving it the right role so that it is more authoritative and you are getting the right answers. Then mention the output expectation very clearly. Give it, do something called as chain prompting, where you are going to mention, ask the LLM to think in steps so that it is able to go ahead and give you what is the reason behind this thinking. And then give examples. This is called as few-shot prompting. When you don't give examples, it is zero-shot prompting. When you give examples, it is few-shot prompting, right? And then mention the constraints. Understand, all of this is called as iterations, because once you give it a prompt, you will understand that maybe there is something missing. I should have mentioned this clearly, and then you are going to iterate on the prompt, and then you'll understand what are the constraints that you need to put, okay? And then I have shared with you some advice and some wildcard prompts so that you can go ahead and get better at prompt engineering and writing better prompts, right? Understand, prompts are your doorway to seize this AI opportunity, right? It is not just plain English. You need to go ahead and think deeper about the prompts so as to get the better answers, right? You have a very obedient and a very powerful individual at your disposal. If you ask it awful questions, you are going to get awful answers. Now, it's your responsibility to make sure that you're asking it better kind of questions, right? Yes.

So this is what we have done so far. I think we have spent 15 minutes just in the revision. Now, everyone, please tell me, go ahead in the chat, if you have understood everything so far. How many of you are aware about this product called as NotebookLM? So in case someone has not used, I'll just give you a glimpse. Okay. So let's say I want to learn about AI Product Management. Okay. This is the topic for today. But sadly, I do not have time. I do not want to invest time. So what I'll do is, I'll go to this platform, notebook.google.com. Okay. I will click on new notebook, right? And now what I can do is, I can give it a lot of documents, a lot of resources, and then I can ask questions and summarize these documents from this interface, right? On ChatGPT, when you give so many documents, it runs out of context, but it is this particular platform, NotebookLM, is working on RAG that I have just told you. So it will be able to answer. Let me show you how it works, okay?

So let's say I want to understand about AI Product Management. So I'm going to insert some resources, okay? So the resources. At HelloPM, we have gone ahead and done something called as the AI Sprint, which has everything that you need to understand as an AI product manager. So I will click on this. Yeah. So I'll click on my channel and then I'll click on playlist and then I'll click on the AI Sprint. And then. Okay, so this is one video. So what I'll do is, I'll copy the URL. I will go to Notebook. I am now collecting the resources. Okay. So my resources are these URLs. So I'll click on YouTube. I will paste the URL here. Insert. So this would be inserted in some time. Second. So now what is happening? The Google, uh, Google NotebookLM is actually taking the transcript of the video. It is breaking the transcript into small, small parts, and then it is vectorizing all of these transcripts, okay, all of the contents out there. Second, I will take maybe, uh, the second video. I will put it again in the YouTube part. Insert. Now I want a blog. So, AI PM this. So I've written a blog as well. So I will go ahead and just add it here. And then I would put a link of website. I can put multiple URLs as well. Okay.

So now I have all of this context ready. These are long, one and a half hour, two hours videos, and this is a very long, I would say, 3,000, 4,000 words of blogs. Okay. So now you understand that, yes, I have all the context available. I can go ahead and ask it any kind of question, and then these questions would be answered from the data that I have given, right? So I will ask a question: "How do I, what skills does an AI product manager need?" Now, this answer would be grounded in what is written in these sessions, right? So if you want to do any research of the world, you can just catch up some YouTube videos, some interesting podcasts from the founders and industry leaders, put up some blogs, put up some reports in PDFs from McKinsey and other companies, and then you should be able to do this. Okay.

Now, it has given me the answer. But this is not the beauty. This is okay. This is cool. But it also has some wonderful features, which is, it can help me create a mind map, right? So if I click on this, it will create a beautiful mind map from all the concepts. Okay. Another important part is reports. So if I click on the reports, these are all the reports that it will create. It will create a complete blog post which I can read. It will create a study guide so that it will be having a short answer, quizzes, some revision material, everything. Then a briefing doc so that I can go ahead and just understand what is important in these particular three, four resources. And then there are multiple things, okay, concept explainer, process overview, and everything. So it is now able to understand the content, and based on content, it is suggesting me that this kind of reports are possible from this, and I can also give my own report, right? So let's say I want to get a briefing. So now it will take some time in order to generate this particular report. Okay. And then I can also go ahead and create mind maps, flashcards, quizzes, and everything. And also eventually, I can also create an audio overview. Okay.

So this is what this product does. This is almost a number one research tool based on AI, which almost every product manager and every individual should use. Okay. So this is NotebookLM for you. There are so many use cases. You just think about it, and NotebookLM should be able to do these things, right? So my recommendation to everyone would be that after this session, go ahead and try to play around with NotebookLM. Super helpful tool, right? Yes. Everyone, a quick yes or no if you're able to understand this tool. Amazing. Thank you. Yes.

So now, let us try to understand, and also what I'm telling you is not just how NotebookLM works, but also any of the tools, any of the AI tools which are based on documents. So in NotebookLM, we are giving it a lot of documents and transcripts, and it is working on the same. On Notion AI, we are actually giving it all of our databases, the Notion documents that we have created, it is working on the same. In Salesforce Einstein, we have all the documents that we have created in our Salesforce, all the sales records, all the CRM, all the emails, all the customer conversations, and then there is an LLM on top of it. So the mental model is that if you have a lot of data, and if you want to put an LLM on top of it in order to make it more queryable with the help of natural language, then this is what you should do. This is the same architecture for all of these products. Okay. Yeah.

So what we do is, we follow the same format. Question number one is, what problem does it solve in people's lives? This is something that you need to answer as a product manager before even building the product. So why does NotebookLM help? Why is NotebookLM helpful? Because it helps people conduct research by helping them read so many documents and summarize these documents at once. It helps people read and learn about exhaustive full topics within a lot less period of time. It also helps people create some effective notes for their learning, for their research, for building any product, or for doing anything. Okay. So there is definitely a value in the product, right? So it will help people become more productive and take better decisions in a shorter period of time, right?

What is the business value? Now, Google has understood that people are using ChatGPT a lot, and Google has understood that now, if people keep on using ChatGPT and Perplexity, the research that people used to do on Google is now going to get reduced. So they have to create a product that will make sure that they are getting their lost foothold. And now NotebookLM has become popular that people have started using it. So it is a strategic bet for Google to create this kind of product, right?

Third is, how do we make it intuitive to use? So Google has made it very simple. So you can see the UI. It is very simple. It tells you. So Google might have thought that, why not we go ahead and allow people to just enter content? Okay. But content could also be in the form of YouTube. So they explicitly mentioned that, yes, people want to learn from YouTube, so you can enter YouTube. People want to upload their PDFs. So they are allowing you to upload the PDFs. So they understood that from what kind of resources people would want to ideally get the information, and they have enlisted all these resources. Similarly, they were able to understand that most of the people are very lazy in terms of prompt engineering. So they are giving you some automatic templates of reports. So rather than you thinking that, "What is the question that I ask from this document?" they will give you automated questions in the form of reports that I have just shown you, right? So this is how you make the product more intuitive, right? And if you guys want to learn how to make the product more intuitive, a simple exercise is, go ahead, look at 10 top AI products, and then reverse engineer what are the things that they are doing in order to make it more intuitive. You can follow similar kinds of principles. You will be ahead of 80% people who do not know anything about UI, right?

And then we have, how will this be engineered, right? How will this be engineered? This is the structure. Please look at this very, very carefully. Okay. Yes, we have 510 people in the chat. Everyone, please write "focus" if you are ready to go through this. Please write "focus" if you are ready to go through this. Amazing. Great. Cool. So focus here, everyone. So here what is happening is, first is the resources. Resources are in the format of articles, videos, PDFs, and text. Which is, videos, it will not be able to understand. So what it does is, it takes YouTube videos, and YouTube videos comes with a transcript. So it is understanding the transcript and not the video. Okay. Second is PDFs. To convert PDF into text, we can either, if the PDF is created digitally, we can go ahead and convert it into text, like through some PDF extractors. However, if it is an image that we have taken photos of and then we have converted the PDF, then we have something called as OCR, or optical image recognition, character recognition, right? So these things would be creative to understand. It is not just that it is building an AI product is only about implementing the LLM. You have to do this dirty work as well, which is making sure that all the content is in the right format, which is text, right?

After this, after this, and even if, let's say we have audio, right now, I don't think it allows audio, but if it allows audio, what it will do is, it will get the audio from you. It will give it to the back end, like from the back end, it will give it to Google Speech-to-Text API or Whisper API, and then it is going to get the transcript, and then the transcript is going to be added here. Okay. So as soon as you add the sources, the sources are extracted, they are chunked, and then they are chunked at two levels: document level and the paragraph level, right? So it tells you that from which document and which part of the document is this content coming from. So whenever you are storing these chunks, there would be a link to that document, whole document, like the source, and there will also be a link to that particular part of the document, which is the chunk, right? I have shown you this example yesterday with that thing, right?

After this, we have embeddings. So now this will be stored in Google's own database for embeddings. And understand, whenever you change the content, whenever you update the content, this is going to get refreshed, right? Till here, we have not used the GenAI so far. We are only using engineering, computer science engineering so far, and some machine learning algorithms. Right? Now, what happens? Your platform, NotebookLM, is going to understand what this content is all about. So it will try to go ahead and create high-level summaries of all the documents that you have created, like high-level summary for every document automatically, and then it will try to understand what are the different kinds of reports that I can generate. And for generating these reports, it would have some kind of templates already. So let me show you. So this is what Google might have created at the back end. Okay. So let's say if we think that there could be a summary that we can create. So it will go ahead and give this kind of prompt, which is: "Summarize the key ideas in plain English, focus on main findings and implications." If they need to create a study guide. So here what is happening is, we have these report prompts, right? It will show you that these are the things that you can do with this report. And then the user input, whatever the user has inputted, the prompt, and the retrieved context. Retrieved context means whatever is matching with that prompt. Whatever the words that you have mentioned in that prompt, whatever is matching in the vector database, that is going to be fetched, and then that is be, that will be sent to the LLM. If it is Google, the LLM is most probably going to be Gemini. So this is the prompt library. Okay. So if the summary type, the report type is summary, then this is the prompt. If it is a study guide, then: "Generate a structured outline with key components, definitions, and questions for self-testing." Mind map: "Organize the main ideas hierarchically with branches for subconcepts and examples." Right? And then reports would be generated, and then reports could be summary, mind map, guides, FAQs. And then what will happen? You, as a user, will give you the feed, will give the feedback that it is good or it is bad, and then it is going to go ahead and auto-correct these prompts, and you can also go ahead and put your own prompts.

So what is happening? This is augmenting. This part is retrieval, and this part is generation, right? So we are first creating the documents, then we are generating, then we are, uh, going ahead and giving it to the LLM, and then we are generating and going giving the output to the user, right? So this is how NotebookLM works in a nutshell. It is a pure play RAG application, right? Everyone, a quick yes or no if you are able to understand. H. Now, one question to everyone. Question to everyone. Okay, let's say if I ask the question to NotebookLM that, "Summarize all the contents. Summarize everything that you are, like, from all the resources, summarize." Now, what do you think will happen? Think about it for a moment and tell me in the chat, what will happen then? Let's say if I give it a query, "Give me a summary of this content." What will happen? Now, summarization. Yes. Cool.

So understand, summarization is a non-typical task. Why? Because summarization is the most, I would say, popular task, but it is a non-trivial task. Because in the prompt, what would be mentioned? "Summarize this document." When this is mentioned in the prompt, when it will search in the RAG model, when it will search in the embedding, what will it search for? "Summarize article." And now, how will it find that where is "summarize" and all? Because it is doing a vector-like search. So it might find some chunks where "summarize," "brief executive summary," these kinds of things are written, but the summary would not be accurate, right? So what is the solution? The solution is, we do not implement this normal RAG pipeline in these kinds of applications. What we do is, we have something called as hierarchical, hierarchical RAG. I'm not sure what the spelling is. So hierarchical RAG, what it is, is that I have source one, I have source two, I have source three, okay? So now, first of all, understand the problem clearly. Okay. I have created chunks. Let's say there are 10 chunks that are from this document. There are 10 chunks from this document, and four chunks from this document. If I ask the LLM the question that, "Please summarize this," then what will happen? It will make the vector of "summarize," right? And "summarize" vector might be equal to "brief," okay? Or maybe some other things, like synonyms of "summarize," right? And it will go ahead and try to find "summarize," "brief," and some things in these chunks, and whatever, whatever are the chunks that are relevant, it will go ahead and give these chunks, let's say one chunk from here, one chunk from here, and one chunk from here. So we have got three chunks, right? But understand, these chunks are incomplete. They have mentioned "summary" or "brief" or "in short," something, but they do not contain the true summary. They just contain these words, right?

So what we will do here is, we maintain a summary. We maintain a summary at a high level. So for all of these documents, as soon as you upload this document in NotebookLM, it is first going to generate the summary of the whole document. So it will send it to the LLM. It will generate the complete summary so that it understands what the report is. Then for the other, it is going to generate the summary. For other, it is going to generate the summary without you saying anything. And then whenever you send a query of summary, it will understand what the query is. And then it is going to answer by concatenating or copying or taking all of these summaries into the context. That way, context is also not increased, but there would be some more LLM calls, right? So how we have solved this problem? We have taken all the sources automatically without even the user saying so. We have taken the summaries of every individual source, right? And once we got the summary from the LLM, now whenever a user is asking a question about summary, we are going to combine all of these summaries in order to get a better answer, right? Yes.

Everyone, a quick yes or no if you are able to understand this. And like this, guys, although RAG is simple, okay, RAG is not rocket science, but you have to understand the nuances. Every product is unique, so you have to understand these kind of nuances before going ahead and calling yourself an AI product manager, okay? So this is about how NotebookLM works, and all the RAG applications work in a similar format. You have the knowledge base, you convert the knowledge base into the vector database. After that, you go ahead and get the user query, you match the pattern, you get the top similar chunks, and then you put the LLM on top of it along with the system prompt, right? Yes.

Now, in the context engineering, we have so far talked about RAG. We have talked about prompt engineering. And the third part of context engineering is actually fine-tuning. So sometimes what happens is, sometimes what happens is, you have to retrain the model. You have to change the parameters in the model for your use case. For example, Google has this amazing model called as AlphaFold. Okay, Google Alpha, and Google AlphaFold or something is the name, AlphaFold, I think. Okay. So what it does is, it is able to identify the patterns in protein, and then it is able to suggest that what is going to be, let's say, the consequences, which is the diseases which are identified from protein patterns, and so many things. Okay. But understand, when ChatGPT or Gemini was trained, it does not have any information about proteins and all. It has the information about how normal natural processing works, but it does not have any understanding about how to understand the protein models. Okay.

So now what happens is, we are going to take some new data which only contains information about these protein folds, and then we are going to go ahead and retrain our Large Language Model again, so that now we have contextual information about this. So if there is some information that is completely lacking, that cannot be compensated with RAG, then we use fine-tuning. Plus, sometimes when you have to change the personality of the model. For example, this is a real use case that one of the bigger banks, they used ChatGPT in order to answer the customer queries, right? And then they were able to find that ChatGPT is able to answer well, but it does not have that personality of how their people were actually going ahead and answering. So what they did was, they actually taken all of their past records of the chat which humans have done, they have fine-tuned GPT and other models on the top of the same, and then they were able to get a new model which is now able to act nicely. Similarly, Bloomberg has created a product called as BloombergGPT, where they have trained complete Bloomberg on top of, uh, a GPT on top of Bloomberg data, so that if it has that kind of context, right? That kind of information and knowledge.

So fine-tuning is adopting a pre-trained Large Language Model, which is a ChatGPT or any base model, with a domain-specific data to make it more useful for a particular task, right? So what we do is, we take a base model, Llama, GPT, Sonnet. Then we do the fine-tuning with curated data and examples, and then we have a fine-tuned model. Okay. It is one line of code which you have to use in the OpenAI terminal. OpenAI API fine-tune create, data.json, JSON, whatever the data that you have, then this is the model that you want to train. One line of code can go ahead and do the fine-tuning. But understand, fine-tuning is the costliest of all the three methods that we have talked about. Fine-tuning takes a lot of money, a lot of resources. So unless you are not sure, you should not start with fine-tuning. First, do prompt engineering, then try to understand if the problem can be solved with RAG. If it is a lack of information problem, if it is not solved, then you should go ahead and go to fine-tuning, right?

And generally, fine-tuning is of two types. One is full fine-tuning. Second is parameter-efficient fine-tuning, or PEFT. Okay. So in full fine-tuning, what happens is, whatever are the billion parameters that we have in the Large Language Models, we are going to try to update them all. So it is very intensive, right? So this is going to give you great results, but it is also going to be very, very intensive on the CPU and GPU, and it is going to cost you a lot of money, right? Similarly, we have parameter-efficient fine-tuning. Okay. Where what we do is, rather than, so I have talked about parameters in the last class. In parameter-efficient fine-tuning, rather than going ahead and, uh, optimizing and changing every parameter, we only change few parameters to make sure that the model is able to get that behavior without spending a lot of money on training, right? So it updates a subset of parameters. It has lower cost and is faster, right? And the training can be done faster. The examples are Adapters and LoRA, right? So this is called as fine-tuning in LLMs.

Now, a major question is, when should I choose what to do? Whether I should do prompt engineering, or RAG, or fine-tuning? How should I choose? So this is a simple decision tree for you. Okay. If you want to decide the right contextualization method, this thing is called contextualization or context engineering. If the base LLM is good enough with careful prompting, then go with prompting. Experiment. Try with prompt first. Before going to advanced methods, try with prompt first. If it is giving you the answer, go ahead and do this. There are multiple products that are based on the same. For example, Granola and ChatGPT, they are only working on prompt engineering, right?

Second is, if it is not working, prompt is not enough. Then, if the problem is only about missing or dynamic knowledge, then go ahead, use RAG. Try Notion AI, NotebookLM, they are using RAG, right? And the third is, if the issue is about style, tone, or a domain-specific behavior that you'll understand by asking questions to the LLM, then you should go with this. And examples are Duolingo Max and BloombergGPT that I have already given you examples of, right? So guys, this was context engineering. Everyone, tell me yes or no in the chat if you are able to understand everything so far. We talked about prompt engineering, we talked about RAG, we also talked about fine-tuning, right? And the question is, the question is that what you should use, what you should use is usually decided by your context in your problem. Okay? Don't think that if some company was able to use something and get the results, you will also get it. Please try to understand the user problems, the context, the limitations, the trade-offs, and then choose whether you should go with prompt engineering, RAG, or fine-tuning. You should always start with prompt engineering. If there's an issue with data, go with RAG. And then if the issue is with personality or the base data, then go with fine-tuning, right?

There is also an interesting thing that is coming across these days. I'll just give you a hint. We talk about this in detail in our bigger program, which is an amazing thing which is called as transfer learning. Okay. This is an upcoming, very, I would say, very promising field, and many companies are going to adopt this. Just a moment. Yes. So there is something called as transfer learning. So what we do is, the issue with Large Language Models is that the more intelligent the model, okay, the more it is going to cost, and the more it is going to have latency, which is it is going to take more time to respond. So if you go to the OpenAI API, okay, OpenAI API cost, okay, so if I go to pricing, understand that this GPT-5 is their best model, and the cost of GPT-5 is for output $10 per 1 million tokens, right? And then they have a small model, which is GPT-Nano, which is $0.41, $4 for 1 million tokens, almost 25x difference, right? So models which are small models have less number of parameters, are more efficient and cost-effective, but they do not have the right kind of, they are not trained on more data, and they do not have more parameters, that means their output could be a bit here and there, okay?

So we have something called as transfer learning. What we do is, we pick a small language model such as Gemma or a combination of, or something between the small and the large language model, such as Nano, which is GPT-Nano 5 Nano or 4 Nano, whatever you want to choose, okay? These things have less cost. What you will do is, you will go ahead, generate some synthetic data from the top models, which is GPT-5 or Gemini 2.5 Flash, okay? You are going to use Gemini in order to generate some synthetic data, and then you are going to generate that data, and then you are going to fine-tune these models on that data, so that these models, although they will not get as intelligent as GPT-5 or Gemini 2.5, but still, if you give on specific use cases, for example, if you are using for customer support, if you are using for coding, if you are using for coding in a particular language, if you are using for a particular use case, then you can generate enough data, and then you can retrofit these models with fine-tuning, and then there is going to be a one-time cost of fine-tuning. But then eventually, your overall cost of API and inference will reduce. This is called as transfer learning. So you have a small language model which is efficient, which has less cost, but it does not know about certain knowledge. So you will generate that knowledge artificially from bigger models, put that knowledge into fine-tuning data for these small language models, then the small language models will become more powerful for your context, right? So imagine these Large Language Models as swords. But sometimes for some purposes, you don't need a sword. You just need a needle. And then.

Transfer learning gives the power of sword to this particular needle. Right? A quick yes on everyone if you're able to understand the power of transfer learning. You can explore more about this. We talk about this in more detail in our bigger program. Super helpful skill for any product manager because implementing LM and understanding is is is is easy but getting the most out of from this is super important. Yes. Cool.

Now with that being said, let us go ahead and talk about the interesting piece in the room. Okay. Which is AI agents. So far the AI agent was like your most interesting, your most uh intelligent manager. They are telling you that this is what you should do, this is what you should do, this is what you should do. But they are not taking action. And nothing is more frustrating than a handsoff manager which has they don't know how to do the work but they always try to give you knowledge and gam okay and LM are the same. You give them the input, they give you the output and they appear the most intelligent part. But they cannot act. Act means you do not want the LLM just to write an email. You want them to send the email and take the follow-ups. You want them to do your work rather than just being your uh uh uh your brainstorming buddy.

So in order to give agency to the agent in to the AI, we have something called as AI agents. So AI agent is nothing but a very simple concept. Okay, which is we give access to some actions to our agents. We get our AI to act. So what we do is a simple example of agent is let's say I post very frequently on LinkedIn. Okay. And if someone wants to go ahead and be updated with what is happening in the product management world, what they can just do is go to my profile, look at what I have recently posted and then you should be able to understand what is happening in the AI product management world. Okay, but this thing you have to do manually. What if there would be a large language model? What if there would be a software or an agent that can go to my profile every day in the morning look at the five recent post that I have done let's say not every day but let's say in a way in a week in that week whatever I have posted they should collect all that post and then they should give all of this post to a large language model which can summarize all the important content and then it can send me an email. This is an example of a large language model. This is an example of an AI agent. Okay, which is I am choosing a time where I am going to crawl a profile, LinkedIn profile. I'm going to get all of their post. Then I'm going to summarize this content and then I'm going to get them to send an email. The summarize part is the intelligent part that is done by AI. That is why it is called as an AI agent. Right now I can go ahead and tweak this AI agent more. What I can do is I can allow tell the AI agent that rather than getting the post from someone, you only go ahead and send me the message rather rather than crawling all the post, you should only go ahead and crawl the post where something is written about AI product management. So now the AI agent will go ahead and take some calls based on or take some decisions based on the content that it finds. So when the AI agent is taking some decisions based on its own context then it is called an autonomous AI agent when it is just going ahead and following the instruction it is a workflow agent right a quick yes everyone if you're able to understand the example of AI agent cool and we'll actually create that agent today okay so intelligence autonomy and uh action Right? And there are multiple platform guys understand building AI agents these days is not rocket science. It is commoditized information. How to build great agents is something that is something that you need to learn. Okay. So if you go to platforms such as agents.ai, ZPR, NAN and OpenAI has very recently created their agent kit. You can go ahead and create multiple agents. Okay.

However, there has been a report recently. There has been a report recently and this report got way too popular and it has mentioned that 95% of most of the AI initiative most of them are AI agents have failed and the reason being there was a learning gap. Learning gap means these project did not fail because these people were failing to engineer agents. They were able to create agents but the problem was these agents were actually not solving any good enough problem and it was not learning from the feedback right so anytime you want to go ahead and build any kind of product please remember this if you are creating AI agent if you are creating a normal traditional product or even if you are creating an offline product please make sure that you start from these two If you read this report, I'll I'll attach this report in the resources section when we are sending this to you, you will understand why not having the right product sense was a major problem why these things fail rather than just not knowing about the AI. So understanding AI is easy but using AI at a scale is a different beast altogether. Right? Yes. So this is simple about AI agents. I'm going to help you understand how AI agents are built. So, a quick yes or no everyone if you have ever built an agent. A quick yes or no if you have ever built an agent. Cool. A lot of nos. Cool. So, I I'll Okay. So, understand is also a fantastic flow, but I'll show you something even simpler. Okay. So you can go to this app called relay.app. This is free with enough credit. So I'm using this. Okay. And it is very simple. Okay. So I'll click on login. I might be logged on already. Okay. So I'm already logged in. Okay. So what I'll do is I'll create a simple flow. Okay. So I'll click on new workflow. Now every agent starts from a understand guys. uh this agent I am telling you these agents that you can build the production grade agents are different the products that I'm going to give you examples in just a moment they are also agent but right now we are using like a agent that you can also build on your own okay this is for your own productivity okay so the agent is yes so the agent is will do the simple thing at 8:00 a.m. In the morning it is going to crawl my profile or any other profile that you want to. It will take all the posts and then it will summarize these post and then it will send me an email. That's it. Okay. Summarization will happen with an AI. So add a trigger. Trigger means when should this event happen? When should this event happen? Okay. So I will just go ahead and click on schedule trigger. It should happen tomorrow or at today's 26th today at 8 a.m. So this time has already passed. I can make it tomorrow. Okay, it can run at this time and it should run daily. I can also make it any other time. Okay, this is how the event is triggered. Okay, and then I'll click on done. To make it simple, I have chosen this frequency. But you can also execute your agent on any other event. For example, when you send a message to Slack, when you send and get an email from someone, when you go ahead and maybe send a message on Telegram, when you go ahead and click a button, when you go ahead and get a new lead from your CRM, you can go ahead and add any kind of triggers. Okay, this is just to make it simple. Keep it simple, right? Then we'll add a step. The other step is I want to add LinkedIn. So on LinkedIn I will just click on add this tab. I will search for LinkedIn and then get LinkedIn comments person post. I want some people some person's post. I'll click on this and then I'll select a URL. So what is my LinkedIn URL? I'll go to this LinkedIn slashin / ankit. So this is my LinkedIn profile. I'll just click on this. I will enter manually. I will enter this and then I want to get the top 10 posts and then if posts are not returned then continue without a result and then done. Second step is done. The third step is I have to summarize. So I will summarize and then what will I summarize? I will summarize posts. Next it will automatically tell me what you want to summarize based on what I have sent from the last step. Okay. And then this is the prompt. Okay. I can do it better. I can go to chat GPT and I can ask chat GPT that write me an effective prompt to summarize my LinkedIn posts. Right. So I will go ahead and copy this. Right. I'll go ahead and copy this. Okay. But to keep it simple, I'm not using this. But you can just go ahead and copy this and paste it there. Okay. I'll go ahead with the simple one which is summarize the attached post. Use fewer than 100 words. Okay. Then access to this this this this is this this. Okay. Plain text. Okay. Done. Right. And then we have plus button which is I want to send myself an email whenever this happens. Email send email to yourself. What is the subject? Summary from Ankit's post add body. If I write X I should if I rect the rate I should be able to get the sum AI output summary is above okay and then I'll go ahead and click on done right and now okay so everyone so far write yes or no in the chat if you're able to understand the flow so far it is super simple Right. Correct. And now what we'll do is we will just go ahead and try to test this start now. I can test this, right? I don't have to wait for morning tomorrow because you guys would not be there. So I will test this summarize with AI and then an email to yours. So I'll click on this. This is the email. The post primarily promotes a free multi session AIPM master class which drew over 800 live attendees. The master class focuses on mastering LLM. This this this and this. I hope you guys remember this right now guys tell me confident in the chat if you are confident in creating your own agents now tell me confident in the chat. Yes. So understand this tooling was never difficult. This tooling was never difficult. What one thing that AI has done is it has gone ahead and made things simpler for everyone. It has gone ahead and made things super simpler for everyone. Okay. As a person, you need to understand that now you have so much power and you have the power to exercise this power. You have the ability to exercise this power. Okay. Now you should focus on the right use cases. Okay. So what I'll also do is I will share with you a list. So Google has created an amazing guide. Google has created an amazing guide which talks about what are the different kind of use cases in different kind of industries for AI agents. I will share this guide also with you in the slides so that you are able to understand and then create agents there. First whenever you create any product understand the users and the business. An AI product manager is a product manager before they are an AI product manager. Okay tools are easy. I have already taught you the tools. they are not so difficult. Important thing is understanding to solve the right problems. Okay. And then when you create an AI agent, these are the things that you need. So this is a general stack that you guys can go ahead and take a screenshot of. In an agent, you need reasoning so that you can summarize, you can take decisions, you can call certain kind of tools, right? And generally LLMs are used. LM's large language models are GPT, Claude, Llama and others. And small language models are F3 and JMA. Right? Similarly, you need memory. For example, if you in chat GPT, it remembers everything that you have told. But when APIs are called in in agents, they might forget about your older conversation. So, you need to save your memory. Okay. So, understand here what has happened in this model. What is memory? Let me tell you. So, in this model, in this flow, it found 10 post then it gathered the data. It sent the data to the other step where the AI summarized it and then it was able to get that memory that data that summarization to the next step. This is called as memory. Making sure that LLM and or the whole uh model the whole agent is able to remember what you just said. Right? This is called as memory. And for memory we have multiple tools. For memory we have vector databases such as pine cone, chroma, rag and feedback loops. Right. And then there are tools. Tools can be accessed with the help of databases, visual agents and APIs. APIs are available. There is something also called as visual agents. Visual agents is it will if it does not have an API, it will act like a computer. It will browse, it will click buttons in order to get the data for you. Okay. And then so open world operator is example of a visual agent. Right. Then we have orchestrators platform on which you can create these kind of tools these kind of agents. So langraph crew AI NA10 is these platforms. I have given you a new example today which is array.app and then we have guard rails. So now understand AI agents are like fire. You can use them to cook your own food. You can use them to show away danger animals. And you can also use it to burn your own hands and burn your own home. So guardrails are super important. Guardrails make sure that your content is relevant, your agents are safe, you have you are not abusing the tools and then you are also validating the output before presenting it to users. Right? In our major big program we tell you about in detail of all of these things so that you are able to build some secure agents, production grade agents, right? Yes. And uh these are some examples of agents which are useful. Customer support agents which will read tickets, drafts, replies and then update the CRM meeting productivity such as fireflies, fireflies and granular that we have discussed yesterday and bizops business ops which is sepia or enterain agents that monitor incoming data and execute automations. Right? So this is in a nutshell what agents are. Okay.

Now in the next part of the class we are going to talk about how platforms such as V coding platforms such as Lovable Berchant on Vero works. Okay. But right now let's take a quick break. Okay. So before that everyone tell me on a scale of 1 to five how good are you able to understand everything so far? Great. Cool. Amazing. Cool. So now everyone a quick break of 4 minutes and then we are going to come back. Quick break of 4 minutes and then come back. I think we are right on time. How much is the time? Oh 911. Oh 911 is the time. So great behind lovable. Cool. I'm sure many of you might have gone ahead and tried your hands or at least heard about these apps like these very high growth apps called as lovable, bolt, emergent, fezero. What they do is you give them a prompt then they work in the back end in order to create a full-fledged application for you. Right? What these app do at the back end is something that I'm going to tell you today. Okay, I'll tell you at a high level how these things are supposed to work. But before that we should follow the follow the rules. Okay, the first thing is what is the user value? The user value here is that as an entrepreneur, as a product manager, as an engineer, I either do not know how to code or I do not have enough time to spend on a unproven idea. So what I do is I give it a prompt. I give I want a tool that can help me build a full-fledged app that can help me test my idea. Before these kind of apps, before AI, what used to happen? I will hire a developer. I'll build something and then it will take me a lot of time. Then it will take me a lot of money and then maybe I'll know that this thing is not working. Right? Why? Because you only know if a product is working when you go ahead and launch it. So it was very difficult to build full-fledged apps without the involvement of a developer. These low code tools that used to happen before that like web flow and all they were not very effective for any non-engineer. They still used to take a lot of time. So now with the possibility of AI we have some tools with which we can just go ahead and give it a prompt. It will be able to go ahead and generate a full website or an app. Right? So value unlock is definitely there. Okay. But you should define what is the market that you are playing in. Is it engineers or hobbyist or entrepreneurs or non-coders that will define the UI and the UX right and the strategy. Why should the business pursue this opportunity? There are so many people who want to build their own products, right? And traditionally these people have not do not have the power to do this. Why? Because technology could not support it. Now what has happened right now is that there is a technology called as Gen AI through which this is possible now. Okay. So now there is a huge business opportunity out there. You can do the sizing of the market in terms of tam but there is a big market opportunity right and many product managers many entrepreneurs they want to build quick prototypes before they can go ahead and full-fledged take the business the engineering resources there is a business value the third part is design how do we make it intuitive to use so if you look at the platform such as lovable you'll be able to find multiple things okay first is these These platforms give you a very good interface where you can just write a prompt like a chat plus they will let you to add any screenshots. So most of the people they are inspired. So they will go to a website look at the website it is looking good they will take a screenshot and they want to make something like that right and then they are also going to give you examples of how other people have done this. So you are getting some inspiration. Okay. Plus they also make sure that there is a community which can go ahead and help you. So you can go ahead and get added to this community and then you can go ahead and learn about how other people are using Lovable. So it makes sure that you are able to get on boarded on the platform very very easily. Right? And then they also have some learning tutorials and all that makes sure that it is easier for you to use. And they have also made sure that they have a free pricing plan. Yes, I already have a paid plan. So they do have a premium plan through which they can you can go ahead and try the platform before you go ahead and start spending your money. Right? So this makes sure that the platform is usable. The ultimate usability is coming from the simple fact that an LLM can allow you to generate like convert your simple prompt into a full-fledged website or a web app. Okay. So this is the usability and then how does it work? Okay, how does it work? So this is the simple architecture of these kind of apps. Okay, the user will give the input. Okay, the user will give a input that please create a clone for Airbnb. Simple but they are going to enhance it with a system prompt. So they will write a prompt like you are a senior level system architect and a backend developer and a front end or a fullstack developer and now you have to create the app the product that the user is requesting. Make sure that you are going ahead and following up with uh you are going ahead and following all the engineering principles and using these kind of files and they would go ahead and give more context. Okay. So your prompt whatever you are giving will be enhanced with the system prompt and then as a result what you will have first it will create a prompt to plan the whole product. Step number one is create this file. Step number two is create this file. Step number three is create this function then create an API. So it is going to lay down the complete plan. After that it will create files. It has access to a virtual environment. So now it is becoming agentic. Why? It has an access to a tool. What is the tool? The tool is a virtual environment. So what will happen a virtual environment is nothing a piece of computer that is given to lovable where it can go ahead and deploy your apps. Okay. So it has access to a file system it has access to an automatic deployment. Okay. And then it is going to keep on running the file on itself so that it is able to understand if there are any kind of mistakes and then because it's an agent it is going to automatically correct these mistakes and eventually you are going to get the output. Okay. So lovable, emergent, vzero are nothing. They are just agents which work in a loop to correct the mistakes and they also make sure that they have access to some kind of file system where they can host these files so that you as a user can go ahead and access these files. Okay. So let me give you a moment. Okay. This is chat GPT. I am giving a simple prompt to charge GBD. Okay. I want to create a clone of Okay. help me create a system prompt that I can use in my vibe coding app where user can enter their idea of app and the system prompt will make it detail. scale effective to be able to create a dependable modern app with let's say JavaScript as the primary language. Most of the apps you will see that they are written in JavaScript. Okay, I'll just click on this and then I'll get a very detailed system prompt. Yes. So here what will happen? While the prompt is being generated, what will happen here? I will create a system. If I want to create a clone of lovable, I will create a system. This is the box. User will input something. This request will be added with my system prompt and the user input will be added and then it will be sent to an LLM and then I'll get the complete set of instructions. Right? So this prompt I have taken. Now this is a prompt system prompt. This you are a Yoder. This this this is this this this. And now you understand everything is mentioned here. Right. This now will be sent to the LM. Now based on this files would be generated. So it will create a plan. In the plan there would be some files, there would be some testing, there would be some functionality and then this will be again sent to the prompt. Right? You can actually create your own lovable, your own emergent with the help of relay app because there also you are executing instructions step by step. Right? So this is how lovable and every platform works on the back end. So agent is nothing. It is thinking and accessing tools in a loop. So they have orchestrated them in such a way that they are able to go ahead and execute things again and again in a methodical or a step-wise manner. Right? Now agents are built on multiple patterns. One pattern is that there is one agent who is doing multiple task. Then let's say if I give you people an assignment that tomorrow you have to create a case study on lovable five people in the same team. Okay. Now what will happen? I believe that if five people are working on the same team the quality should improve. That is why I'm putting five people and I'm giving you less time. For an individual I would have given one week but for five people I'm giving two days. So now because I think that efficiency will increase. So now what you guys will do is you will divide the work among yourself that I will look do the research you do the direct someone is going to do the design someone is going to do something else. Okay. And when you go ahead and come back you are going to go ahead and combine all the out outputs and then you are going to create something which is a one case study. So this is how agents also do the orchestration. So there is also design patterns in agents which define how different agents are going to work with each other. Okay. So this is in a nutshell what agents are. Okay. Everyone tell me on a scale of 1 to five you're able to understand agents. Yes. So please everyone please understand things from first principles which is agent is nothing. Agent did not existed. AI agent did not existing 4 years ago. Okay. So it's very simple. If you are creating a workflow which can have an AI LLM in between which can execute actions for you and do some intelligent task that is an AI agent. Right now after this there is one problem with the agents. The problem is that the agents in order to act they need to connect with tools. LinkedIn is one platform, Gmail is one platform, Google Drive is one platform, maybe your SMS is one platform, your Telegram is one platform and there are so many platform, your Slack is one platform. So they need to connect with many tools. And if you are someone who is building agents where LLM needs to connect with the agents with the tools, then it's a nightmare to read every API and connect your product with the API. Okay. So if so many let's say like one LM has to connect with multiple products then everyone has their unique API. So that is a nightmare. So now what these people have done is enthropic the people behind claude they have created a simple thing which is they told everyone that if you want to they have told Gmail slack Jira everyone and the whole world that if anyone wants the LLM to use their APIs please go ahead and create something called as model context protocol or MCP. So what is an MCP? MCPS is these people have created one more versions of their APIs or a layer over their APIs which can be understood by large language models. Right? So maybe yesterday only or a couple of days before Zumato has launched their own MCP. So Zumato is an Indian website where you can is an Indian app or website where you can go ahead and order food. So this is the detailed article on the same where is that? Yes. So now you can go ahead and do so many things from your cloud. So on your cloud desktop or your chat GPT desktop you can go ahead and install this uh uh this cla this summato MCP and then you should be able to go ahead and write these kind of queries Indian food in your Indraagar pizza under 200 rupees and then you should be able to make an order from there only. Right. So now Zumato has an API which already existed for a long period of time but you cannot have conversation with an API through the LLM through natural language. Now they have created an MCP. What does an MCP do? MCP converts the natural language helps the LLM to talk with the API in natural language. Right? That is an NCP. Okay. And there are multiple I'll share this link with you so that you are able to go and understand how it is created. Understand it's not difficult to create MCP. What you need to do is if you are a product like Zumat or any other product in the world, you would have an API documentation. Take that API documentation, go to chat GPT and ask it to convert into an MCP. It will give you a file that you have to host on your server and then you are done. You have to do a couple of testing as well but that is done right. So that is model context protocol. So in model context protocol we have three components. The first part is the AI host where you are going to send query. So it could be claude, cursor, chat, GPT. This is where your MCP client is going to be there. This is your MCP client. Okay. Then there's an MCP server that is hosted at the person who has given you the MCP which is Zumato or Razer Pay or something. And then there is a data source. This is stored at Zumato or somewhere where you can go ahead and have the data or you can take actions. Okay. So any MCP server will give you a small file that is called as client configuration file. You have to enter it into your cloud or any of the LLM that you are using and then your agent will be able to talk to these tools without understanding the API. Right? So this is MCP. You can go ahead and read more about it. Right? Yes.

So we have talked about so many things so far. Cool. Everyone tell me how would you describe the session so far in one word so that I know if we are going in the right direction. Tell me how would you explain this session so far in one word so that I can go ahead and tell you something more. Yes. Great. Now let me just go ahead. Thank you. Thank you everyone. Thank you for the nice words. Now I think we are on time. So we have some more time. So I'll give you one more thing. Okay, which is evaluations. Look at this very carefully. Okay, so there are some challenges that happen in building AI products. The challenges are they hallucinate. They have certain kind of biases and they are indeterministic. Okay. But understand nowadays you people would be understanding that language models are becoming more and more intelligent. Okay. If you find that there is an error, you can ask it to correct that error and it corrects that error. Okay. But still large language models are these powerful beast. If you leave them without a leash then they can do a lot of disaster to your customers. Okay. So this is what AI believes like. Okay. It believes that it has all the content in the world and it can give answers like wrong answers with complete confidence. Okay. So that is why we have something to tame these beast. The leash that works on your LLMs is called as evaluations. Okay. And what happens is whenever your LLM is not working and if you're not getting the right kind of output, there are certain issues and you can resolve these issues with three things. Okay. So when you are building any kind of GI product, you have to take three important decisions. The one important decision is what is the model that you are going to choose. If you choose a very high quality model then you have to pay more cost. If you choose a very low cost model then you have an issue of accuracy. Right? Similarly rag instrumentation in rag there are multiple components. If your chunk size is not correct then the information that it is giving you would be incorrect. If you understand I have told you that there is a vector embedding but for vector embedding also there are multiple algorithms which algorithm should you use okay it depends on the use case. So what is going to be the instrumentation of your rag? What is the vector algorithm? What is the size of chunking? What are the different kind of model that you will use? That is also very important. Okay. These are the decision that you need to take. And then context engineering. What should be the prompt? How long should be the context? how you should give the instructions. These things are going to determine whether your geni app is working rightly or not. And this is actually engineering. Engineering why? Because there is going to be a lot of course correction. Okay. So in order to do these things, in order to understand whether our geni application is working well or not, we have something called as evaluations. So what evaluations do is they check the output for correct syntax and formats for bias for correctness relevance and other unnecessary checks. Okay. So for example if on notebook LM al on granular we generated a summary we want to understand whether this summary is actually aligned with whatever has been said in the meeting or not. So how do we test this? We test this with the help of large language models. Okay. So what we do is to help you understand I'm going to give you a case study on the AI first job website. Okay. So tell me in the chat how many people are looking for jobs. Tell me in the chat yes or no if you're looking for a job. Yes guys even if you don't know your colleagues and your bosses might be present in this meeting. Now I will give you an idea. Okay. And I'm sure many people would have faced this. Okay. So let's say I am creating a new kind of job website where what I'll do is I will crawl all the websites from the all I will crawl all the jobs from major websites from the internet. So I'll go to hired, I'll go to LinkedIn, I'll go to no, I'll go to Instaire, I'll go to multiple websites. I'll go to indeed and other websites in order to crawl jobs. scroll as in I will go to this website automatically I will get the content and I'll scrape the jobs so now I have a job database I have database of all the job descriptions titles companies and everything right now what I'll do is I will enhance this job with LLM I will enhance this job with LLM and by enhancing what I mean is I will give it a prompt and then what I'll do is I will generate this output so I will enter a job description and then the LLM will give me summary of the job description possible interview question. So from the job description in the company it will try to understand what are the question that can be asked skills that are needed to do this job learning guide for this job and quiz for assessment which is a small quiz that will help me understand whether I'm ready to fill or apply for this job or not. Okay, this is a simple website that I need to create. Okay, tell me everyone will this kind of website will be helpful for you or not? People are looking for jobs. Yes. So, it will give you some more context about the job description. Right. So, now and this can be created. You can go ahead and create this website today also like by using labable or something. Okay. But now there is an issue. How do I know this job description, interview question, skill needed, learning guide, everything is good or not. Okay. So I'm going to introduce one method of so understand these things that I have told you which is all of these things they are done with multiple methods. One of the method to run your evaluations is called as LLM as a judge. So we run our outputs through another more intelligent LLM in order to understand whether it is correct output or not. Okay. So here the issue is that although I'm generating good information but this information can be wrong. Okay. So what I'll do is I'll do this. Okay. So I have crawled a job from the internet. I have stored them in my database. I have ran them through LLMs and then I have created this content. I have created this content. Now what I'll do is I will get all of these things again go through another LLM in order to understand which is called as evaluation that whether this content is correctly created or not. This is called as LLM as a judge and then I will find out whether the content is correct or not. This is a simple method of evaluation. Let me show you how it works. Yes. So this is the evaluation. Look at this very carefully guys. This is the prompt that I'll go to the I'll give to the another LM. Okay. You are an AI content quality evaluator for product management job listings. Given the following, this is the original job description. This is the AI generated summary. This is the interview question. This is the output from the other LLM. Right? These are the skills. These are the concept. This is a quiz. Evaluate the content using the following checklist. Is the summary accurate? Interview questions correct? Listed skills aligned? Are the concept useful? Is the quiz challenging? is any information correct or hell needing and then give me the response in this particular format okay then I'll have a dashboard where for every job description this score would be mentioned then I would know whether my LLM is working correctly or not okay so let's say if I get poor or fair or low or weak what I'll do is I will go back I will again try to tweak these things if my quality is add then I will maybe choose change the model or I'll change the rag instrumentation or I'll go ahead and change the context engineering which is the prompt that I have given okay that is how you improve upon AI product with the help of evaluation this is a super simplistic example of evaluations okay there are codebased evaluations as well there are LLM based evaluations as well generally this evaluation LLM that you use so this is LLM1 and this is LLM2. This LLM is generally a more intelligent LLM which is able to assess. Right? So this is an example of evaluation. Everyone a quick yes or no if you're able to understand this. Yes, there are many open source LLM guys. OpenAI OSS is an open source LLM. Lama is an open source LLM. Right? So this is there.

Now we have almost completed the session. This is some set of tools. If you guys are curious, you can go ahead and start using this in your product management workflow. There is also a detailed video on this on our YouTube channel. Okay. AI for PM toolkit discovery for discovery for discovering what to build chat GP notebook perplexity mix panel textsql. Okay. for delivering at intelligence which is nothing but Jira, Asana, Vzero, Kurser, Postbot, Figma which you can use to create wireframes prototypes very quickly. Okay. And then for distribution you have craftable, chatbased, notion, genex, moage that can help you create marketing materials, collaborate with your team and look at analytics. Okay. So try to explore these tools at your own time so that you are able to become an AI enabled PM as well. Whatever we have discussed so far was for AI like applied AIPM, right? And then one more important thing guys that in this program like so far what I have done is I have given you information I have given you information about evaluations about agents about various tools about rack about prompt engineering about finetuning but information is not helpful for you information is just a couple of prompts away what you need is knowledge. And how do you convert information into knowledge? With the help of action and with the help of feedback and reflection. Right? So what you need to do is please do not limit yourself to only this knowledge. Go ahead and do something. Doing as in go ahead and build something. Okay? You have so much information available across the internet. Utilize and try to go ahead and build something right. If you guys are looking for mentorship then we also have a very detailed program. So understand whatever I have told you in this last two session that is not even 10% of what we teach in our bigger program. So if you really found this information genuine useful I teach a bigger program which is a 15week program on AI product management. Okay you can go to the website you can get all the details out there. But if you have to ask me let's say three reasons why you should join LOPM program against other programs is very simple. First it is the most detailed AI product management program. We first talk about the traditional product management then we talk about AI product management. You can go to the website you can compare the curriculum. You can compare curriculum to any other course in the world. You will not be able to find a more detailed program. First point. Second point is everything is practical. We believe in doing things. So in the end of the program, you're actually going to go ahead and build your own product. Okay. So if you go ahead and go to the website, if you go ahead and go to the website hellom.co, you should be able to find in the curriculum section that this is the whole curriculum. Everything is supported with a lot of case studies and tools. Plus in the end of the sessions, we are also going to help you build your own AI product. This helps you prove to other people and to yourself that you are not an information seeker. You are actually someone who understands how to build products and this is a big opportunity for you. Right. And we also help you prepare for interviews and all that is given. And then yes and then the third part is we are the only program that supports you for the long term. Even if you have completed the program, we understand that some people are able to get the job within the program, some people get the job after the program. We support you for a period of two years. So in these two years, if you have any questions, any feedback, anything that you want to get help from us, you should be able to talk to us. Okay. And every week, we keep on doing sessions to make sure that you are able to learn. Okay. The Yes. Yes. And you can go to the website in order to check all the other details that are out there. The cost of the program stands at 75,000 rupees or $1,000 if you are outside from India. Okay. But it is my assurance that if you go ahead and take this program, this is going to go ahead and give you a lot of content, right? We don't give any kind of discounts, but this is the context. You if you want to get more information about this cohort, just go to the website hello.co and then click on this video. You should be able to get all the information right now an important part. Okay, that is why I've given you this context. Yes, classes are conducted on Saturday and Sunday evening 8:30 to 10:30 for ISD and morning for the US batches. Okay.

So now everyone understand if you really want to get into AI product management you have to build a strong portfolio so that people understand that you have what it takes. Okay. You are actually not a talker but a doer. Okay. So what you should do is in order to create a portfolio you can start very small. You start by commenting and start by creating content about geni. Understand for yourself. Okay. Then do tear down in case studies. Then do product improvements and go ahead and build some like try to go ahead and build some hypothesis of the products. After that build some side projects and then if you have already done some past work you can include all of these things and then you can use in your portfolio. You can go to the hello channel there we have created. So if you go to the hello channel on YouTube we have an amazing playlist called as summer of product. Okay. Or this is the playlist getting started with product management or summer of product where I have explained how to create a portfolio. Okay. And then the last part is this. This is the most important slide. What you should do is please do not limit this information that I have told you so far to just this session. Okay? Don't forget about everything as soon as you leave the session. Please take action. What you should do is reverse engineer top AI products in the same way that I have told you. Understand gaps, frustrations in your personal and professional workflow. Build AI tools and agents to solve these problems. Start writing what you are learning. Tag me. I'll be able to like and post and depost. Right? And then keep a track of AI companies what they are doing. Help them improve, enhance, expand and then follow up. Try to find companies who are do really doing good and then understand their product. Reverse engineer the product. Suggest improvements. reach out to the founders and then follow up with the founders. Most of the time you are going to get some appreciation, attention and maybe a job. Okay, there are many people that we have helped came from completely non- tech background, some senior background, some junior background and they were able to become product managers. You can go ahead and look at this playlist called as transition to product management where you can read their stories. Okay. So this is it about today's session guys. Yes. Uh the flagship program is four weeks of foundation PM content which is strategy and product sense. 8 weeks of going ahead and creating like understanding AI in detail. So understand if I can give you this much information in 2 days what can I do in 8 weeks or maybe 15 weeks and then there are three weeks of building your own product. So flagship program covers the entire AIPM syllabus. If some of you wants to check how does a hello cohort looks like, let me show you something. Give me a moment. Yes, I have shared a link in the chat. This is the detailed video where we have shared the kickstarting call for a cohort. So at hello PM, we try to keep things as transparent as possible. We love that. We love being honest in this really corrupted tech world. So I have gone ahead and shared a link where you can understand who are the kind of people who join, what are their backgrounds so that you are able to go ahead and take a better call. Yes. And there is a lot of free content that is available on our YouTube channel. You guys should absolutely go ahead and check it out. The placement support that we offer is including four things. We offer you we help you create a better resume. We help you build a strong portfolio. We help you prepare better for the interviews and then we also have an internal job board where alumni or network companies and other companies that we seek out to also list your jobs and for many of these jobs referrals and recommendations might also be available. We do not guarantee placements but we help you out in all the possible ways that we can and we also uh are available across the gloes. So we have a lot of people who are going to join from UK, from the US, from Canada, Australia, New Zealand and as well from India. And this happens because of a large alumni network that we have created. You can use self-hosted LLMs without having internet, but you should have a powerful computer. Cool. Thank you everyone. I'll just go ahead and end the session now. Thanks a lot for choosing to spend your weekend with me and with Hello PM. I believe I was able to go ahead and add some kind of value as available from the feedback and the LinkedIn comments. We are very soon going to conduct more sessions. The next batch is going to start from 9th of November. So take your time. Understand you do not have to take this decision in FOMO. Go ahead, take the right decision and understand it is not just a time invest. It is not just a money investment but it's an important time investment. Okay? So take your decision by understanding everything. We want you to win but once you come in the session we make sure that you are working hard. Yes. Cool. Yes. Everyone if you have any custom questions you can go to the website. There's a shoulduling call link. You can go ahead and schedule a call and then the team should be able to help. And we keep on doing these counseling sessions so that you can ask your questions as well. Okay. Cool. Yes. Cool. Thank you everyone. Thank you everyone. Bye. Take care. You have been a great audience. Thanks again for choosing to spend your time with me. I really feel good that I was able to be somewhat helpful for you guys. Thank you everyone. Bye and take care. It was my pleasure hosting you guys. Thank you guys. Take care.

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