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Most people using ChatGPT and similar tools don't even understand what it really is and how it works. Understanding just the basics of AI can make you so much better using tools like ChatGPT, image generators, or even more advanced systems. So, in this video, I'm breaking it all down: what AI is, the types you can actually use right now, and how they work. Stick with me, and by the end, you will feel like an AI Master yourself.
Here's the problem: AI is a total buzzword right now. Anything that seems remotely smart gets labeled as AI, whether it's a ChatGPT mod, autocorrect, or even your fancy fridge that knows when you're out of milk. People think AI is some kind of an all-knowing super genius. But the truth is that most AI systems today are just really good at doing one specific thing. That's it. They are tools, not brains.
Think of AI as a big umbrella. Underneath it, you've got things like large language models, that's ChatGPT, image generators, robots, you name it. At its core, AI is just a system designed to mimic human-like intelligence. It can solve problems, recognize patterns, make predictions—stuff that looks like thinking. But it's not thinking like we do. It has no feelings, no consciousness, no "aha!" moments. It's following a plan step-by-step, predicting what should come next. It's impressive, but it's not magic.
After this video, you will be asking about links to all the tools mentioned. I got you. We have a website where we post reviews of all the AI tools we test ourselves. So check that out by hitting the link in the description.
We, as consumers, now have access to a few types of AI tools that we can already use: large language models, image generators, audio generators, video generators and editors, voice assistants, and productivity AIs. These are the core ones that most of us can use right now. These tools might seem wildly different, but they all work on the same basic principles. And just to set the record straight, what we call AI isn't some thinking machine. It's actually neural networks. Let me explain.
At their core, neural networks are systems that learn patterns in data and use those patterns to make predictions and generate results. Imagine them as a bunch of layered filters. Each layer processes the data, passes it to the next layer, which refines it even more, and so on. By the end, you get the final output.
Now, neural networks don't start smart. They have to be trained. That's where developers come in. They feed the network massive amounts of data, like text, photos, or videos, and the network starts guessing outputs. Every time it gets something wrong, which at first is a lot, it adjusts its internal settings to get a little closer to the right answer. This process happens millions, sometimes billions, of times until the network gets really good at recognizing patterns and generating results. At that point, it's ready to take prompts from you and turn them into something useful.
In my YouTube agency, we use AI for almost everything, but it's not like it does everything for us. It enhances our workflow, making us more productive. And by the way, a video about it is already on the channel.
Now, let's go over each type of neural network you can use right now.
Large language models: ChatGPT, Gemini, Claude, Mistral, Grok. Feels like a new one pops up every week, right? Here's the thing: they all work basically the same way, just at different scales. How do they work? Transformers. Transformers take your input, like a question, and figure out the best output, the answer, using probabilities. Let's say you ask, "What shape is the wheel?" The model breaks that into keywords like "shape" and "wheel." Then it calculates how those words relate. It looks at the data and thinks, "Alright, the next word with the highest probability here is 'circle'." Boom. It gives you the answer.
Why does it get that right? Two main reasons: First, massive data. These models have read so much text, and somewhere in there, they have seen plenty of mentions about wheels being circular. Second, attention. This attention helps the model focus on the important parts of the input, like "shape" and "wheel," instead of random filler. That same process works for anything these models do: writing essays, coding, analyzing data, you name it. But they don't understand like we do. For them, there are no actual words; it's all just numbers, probabilities, and math.
When it comes to prompting LLMs, you might think it's all about creating the perfect structure and nailing the right attributes, and yeah, that's true, kind of. But there's a twist. Every model interprets prompts a little differently. Bigger models are way more forgiving. ChatGPT, for example, is the best out there, and you can basically talk to it in natural language. Same with Gemini. But smaller models, like Mistral or Claude, they might need you to step up your prompt game and be a bit more structured.
That said, the core rules of prompting are pretty much universal. First, be descriptive. Models love big, detailed prompts with all the context and requirements laid out. Don't skimp on explaining what you need. Tell the model what the output should look like, how long it should be, who's going to read it, what style or tone to use—everything. The more you explain, the better the results. You don't want the model guessing what you want. Spell it out. Be clear about the audience, the format, the language, and the main ideas.
Second, use roleplay. It sounds simple, but it's crazy effective. Telling the model to act like an expert in a certain field can dramatically improve the response. It narrows down the data the model pulls from, making the output more accurate, more relevant, and polished. And don't forget to set limits. Make sure the model knows what *not* to include. This is another small tweak that can have a big impact.
And yeah, you can pile all these instructions into one big prompt. If you're using a free version of the model, but if you are in a subscription, you've got room to take it step by step. If you want a deep dive into prompting and want to become an expert at it, hit that subscribe button. A full guide is on the way, and trust me, you won't want to miss it.
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The second big player in the AI world is image generators. These models operate totally differently from LLMs. Sure, they're also trained on massive datasets, but instead of focusing on words, they work with visual elements. The model gets fed millions of images, each paired with a description. Over time, it starts to understand patterns, like how certain groups of pixels represent a cat or tree. Of course, that's a simplified version, but you get the idea.
Once trained, the model knows what each word in a prompt translates to in terms of pixel relationships. So when you type something like, "Generate an image of a fluffy black cat with glowing green eyes," it doesn't just pull an image from a database. Instead, it uses the relationships it's learned to create an entirely new image.
But image generators don't start from scratch. Every time they generate something, they begin with a blank canvas—basically, static noise. Then, through a process called diffusion, they refine the noise into a detailed image. That's why these systems are often called diffusion models. The base image is a chaotic mix of black and white pixels, and if you sum up their values, you get zero. This technical quirk is why AI-generated images often feel a little off. They lack natural contrast or highlights that stand out. If you're wondering if an image was AI-generated, check the contrast and lighting. It's a dead giveaway.
Now, picking the right image generator depends on what you're looking for. DALL-E is super easy to use and great for beginners, but unlocking its full potential usually requires a subscription. Gemini can do images, but it's not the most creative or customizable. Adobe Express is user-friendly with lots of tweakable controls but can sometimes turn out odd results. Midjourney is the gold standard. The web version is solid, but using Discord unlocks more features, though it requires a specific way of writing prompts. Runway is decent for images but shines in video generation. We mostly use Midjourney in my YouTube agency.
And if you're thinking about integrating AI into your workflow, here's my advice: pick one tool for each task and stick with it. Consistency isn't just key for AI; it's key for YouTube in general, too. Many creators start strong, don't see the results they hope for, and end up abandoning the channel with real potential. I almost fell into that trap when I was starting out. So what changed? I built a team. Trying to do everything on my own became exhausting, and it started killing the fun. Bringing people on board turned things around completely. That initial team, they are now the backbone of my YouTube agency, and I've never been happier.
And here's the thing: you don't have to do it alone, either. That's where we come in. My team can help you with every step of the process. We'll handle all the research and come up with fresh, trending ideas your audience will love. We'll create a personalized, optimized content plan tailored to your goals. Our designers will create thumbnails that demand attention, and our writers will whip up titles to get clicks. Need help with scripts? We've got experienced scriptwriters who can nail it for you. Not sure how to set up your studio? Our director can hop on a call and guide you to create a pro-level setup on a budget. We can even edit your videos and take care of publishing. All the SEO, titles, descriptions, and tags are covered. All you'll need to do is sit in front of the camera and talk. Sounds good?
If you're ready to take your channel to the next level, check out the link in the description. Fill out a quick questionnaire about your channel, and we'll get in touch. Let's conquer YouTube together.
Prompting for image generators might feel similar to prompting LLMs at first glance, but the focus shifts a bit. Instead of describing things like audience or tone, you're focusing on visuals: colors, elements, composition, textures, and more. Think of your prompt as a never-ending description of every detail you want in the image. Here's a great way to practice: Take any image you like and start describing it. Write down everything you see: what colors are dominant, how objects are arranged, the lighting, the mood, even tiny details like shadows or textures. When you've squeezed every detail out of the image, boom, you've got yourself a target prompt. Use that as a template to write your own.
Why does this matter? Because image generators can easily get wild with guesses. If you're vague, the results might completely miss the mark. To avoid this, add negatives to your prompt too. For example, if you don't want blurry edges, muted colors, or unnecessary objects, just say so. Some generators, like DALL-E, let you include this directly in the chat, and some models have a separate input field for negative prompts.
There are two types of audio generators: text-to-speech generators and music generators. While they serve different purposes, they work in pretty much the same way. Both are trained on massive datasets, either music tracks or voice recordings paired with transcriptions. From there, it's all about probabilities. The models calculate sound waves for each fraction of a second based on patterns they've learned.
Music generators like Suno, Mubert, and Refusion focus on understanding elements like melody, rhythm, harmony, and instrumentation. When you give them a prompt, they mix and match these components based on the relationships they've learned during training. Whether you want a calm piano piece or an energetic electronic track, the model builds the composition step-by-step.
Text-to-speech models, on the other hand, take your input text and figure out how to turn it into speech. Tools like ElevenLabs or Speechify analyze each letter, syllable, and word to calculate how they should sound together. They then use this data to synthesize natural sound and voiceovers, complete with tone, pace, and emphasis. While they're working on different types of audio, the core idea is the same: learn the patterns, then use probabilities to create something new and unique.
Prompting for audio generators is a simple process, mainly because there is often not much prompting involved. For music generators, many tools don't even have a typical prompt inbox. Instead, you're usually adjusting parameters like BPM, style, and mood. However, some tools like Suno do let you write a text description of the song you want. You can even have Suno generate lyrics, combining its music generator with LLMs and text-to-speech.
If you're using Suno, keep it simple and to the point. Describe the music style, the mood, how it should feel, and maybe the BPM. No need for specific phrasing, just focus on being clear. As for text-to-speech tools like ElevenLabs, there is really no prompting involved. You paste the text you want turned into speech, pick a voice, and tweak the properties to suit your needs. Make it faster, slower, more energetic, just accordingly. Some tools even let you clone your voice, which is a cool extra, but again, no actual prompts required.
Video generators work a lot like image generators, with one key difference: instead of creating a single image, they generate a series of frames that flow together to form video. These models are trained on massive datasets of videos paired with descriptions. From this, they learn patterns and how frames change, the spatial relationships within each frame, and the temporal dynamics—basically, how objects move or transform over time.
When you give them a prompt, they interpret it mathematically and start generating frames one by one, starting with a base image for each frame, similar to how image generators work. There are two types of video generation tools: those that create entirely new videos and those that edit existing footage.
For creating new content, tools like Sora, Hyper, Runway, and Pika fall into this category. These tools generate frames from scratch, following the patterns they've learned during training. For editing tools like Pictory, Thisa, and Fliki, they take a different approach. They first process your prompt using an LLM to create a storyline. That storyline is then broken down into scenes with keywords generated for each one. The tool uses these keywords to search its built-in footage library, selects relevant clips, music, generates a voiceover using text-to-speech, and stitches everything together into a final video.
Prompting for video generators is very similar to prompting for image generators, but with an added layer: motion. You still need to be super descriptive, but now you have to include details about how things move. Does the camera pan, zoom, or stay still? Are the objects in the scene moving? If so, how are they interacting with each other? Give as much detail as you can, but keep it simple and vivid, and don't overcomplicate your prompts. Video generators can sometimes forget parts of the description or mix things up in unexpected ways. Focus on describing the essentials: what you want to see and how you want it to move. From my experience, sticking to the basics while being clear and vivid works best.
For video editors, there is usually no real prompting involved. Most of the time, you just provide a general description of the video or the plot idea. The AI takes it from there, either handling everything automatically or giving you a few options to choose from. These tools are super intuitive, and if you want to get the hang of them quickly, we've got some great videos on the topic, so definitely check those out.
Voice assistants are probably the easiest type of AI to explain. Google Assistant, Siri, Alexa—these are the names everyone knows. Unlike other AI systems, they're not so much about creating content as they are about understanding and acting on data. So, honestly, they're not that smart on their own. Most of the heavy lifting comes from transcribing voice requests and figuring out the best action to take.
They all work in three stages: speech-to-text, intent recognition, and processing, then text-to-speech. These steps use the same tech principles as the audio generators we talked about, but things are starting to shift. Companies are now adding proper neural networks to voice assistants. For example, the new Siri (not out yet) is expected to come with real context understanding, including personal information and the ability to take actions directly in apps.
Luckily, natural language is the main focus of these systems, so prompting is practically non-existent. Basically, you just verbalize your requests however you want, and the assistant will figure out the rest. No prompting structures, no secret tips. You just talk normally and hope for the best.
One more type of AI you can use right now is productivity best bets. These smart tools are popping up in all sorts of apps, helping you write, organize, and just get stuff done more efficiently. Take email clients like Superhuman, for example. They use AI to help you zip through your inbox faster, organizing emails so you can focus on what matters. Plus, they've got built-in writing tools that can rewrite, paraphrase, or adjust the length of your messages.
Then there are platforms like Taskade. They streamline managing workflows and processes, simplifying collaboration and overall keeping you on top of your schedule. These tools can generate project outlines, assign tasks, and track progress, which is practically useful for remote teams. And let's not forget AI-powered CRM tools like HubSpot or Pipedrive. They take the all-boring CRM systems and flip them, using AI to optimize your workflow. On top of this, there are tools like Zapier or Integromat that help connect different apps and automate tasks, making your work life smoother.
So, whether you are drowning in emails, juggling tasks, or managing customer relationships, there's probably an AI tool out there ready to give you a hand. It's all about working smarter, not harder. And by the way, we've already reviewed some of these tools and put the reviews on the website, a.me, so check them out.
Here's the downside, though: there's almost no prompting involved with these tools. Unlike AI systems where you can type out a detailed request and get a tailored response, these productivity tools are more well-locked in. They're mostly standalone setups, and you're kind of stuck with the options they give you. You press a few buttons, choose what you want, and boom. That said, no room for much creativity or flexibility.
There are tools for almost everything: presentation generators, legal document analyzers, recruitment screening tools, coding assistance, financial planners, supply chain optimizers, scientific research aids, you name it. But no matter the AI tool you're working with, the golden rule stays the same: be detailed, be descriptive, and straight to the point. Clear inputs lead to better outputs. And of course, practice makes perfect.
And of course, of course, of course, click the link in the description to partner up with the best YouTube team ever. Thanks for watching, and I'll see you in the next video.
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