Transcription
Picture this. It's 1953 and a television writer named Leonard Stern was stuck on an adjective. [music] He was writing a script for the Honeymooners and he needed the right word to describe his boss's nose. His friend Roger Price walked in and, impatient to work on something else, Stern said, "I need an adjective that." And before he could finish, Price said, "Clumsy and naked." Stern laughed because now his boss had a clumsy nose or a naked nose. But the words were completely wrong for the context, yet somehow still worked, and they had stumbled onto something.
Though it took them five more years to figure out what to call it, they ended up calling it Mad Libs. But what they actually did is stumble upon something that may be the oldest pattern in computing. You see, what they discovered was something extremely fundamental to how we code and, more importantly, how we use AI today. But before we dive into that, I think it's important to look at how Mad Libs works. And I promise this is important for discovering and understanding computing in general. Just stick with me for a second here.
You see, this is how Mad Libs works. You have a template. The template has blanks. Each blank has a type: noun, verb, adjective. Someone who cannot see the template provides words that fit those types, and then you read the results. The comedy comes from the mismatch. The person filling in the blanks doesn't know the context. They're providing semantically correct input without semantic awareness. They give you a noun, and it is, in fact, a noun, but it's the wrong noun for the sentence. That wrongness is actually funny.
Now, here's something that might reframe how you think about software. When you write a line of Python, let's say, you are not talking to the computer. You are filling in a template. This is called [music] an abstract syntax tree. It's the structure your code becomes before anything executes. And if you look at it, really look at it, you'll notice something. It has blanks, [music] typed blanks. This node expects an expression. Another node actually [music] expects an identifier, and another a value. You, the programmer, are providing words that fit those types. The interpreter takes your input and fills in a much larger template that you never see. Thousands of lines of C code with slots for your variables, your function names, your values, and [music] that template gets filled into another template at the assembly level. And that one gets filled into machine code all the way down.
Mad Libs isn't a metaphor for programming. It is, at a structural level, what programming is: templates [music] with type slots filled according to rules, composed into larger templates until eventually something executes.
But templates need to be filled with something, and that something has to be stored somewhere. Which brings us to memory. In traditional computing, memory is a hierarchy. At the top, closest to the processor, you have registers. Tiny, incredibly fast, maybe 64 bits each. A handful of values that the CPU can access in a single cycle. [music] However, when you look a little bit up the chain here, you have something called the cache. Still fast, still small, measured in kilobytes or megabytes. This is where the machine keeps data it thinks it will need again. The principle is called locality. But if you accessed something once, you'll probably access it again. If you accessed this address, you'll probably access a nearby address. Cache exploits that pattern.
Then you finally move into the main memory, or what I would consider the main memory: RAM. Random access memory. Larger, slower, often in gigabytes. This is what most people think of when they think of computer memory. It holds the working state of programs. When you open an application, its code and data load from the disk into the RAM so the processor can access them without waiting for the slow mechanical process of reading from storage like discs or archives. Speaking of discs, you can have solid-state or even spinning magnetic platters, terabytes, persistent, and when you save a file, this is where it goes. Even below that, you have archival storage. This is on tape or cold [music] storage, the stuff that you back up and and rarely touch. Personally, I prefer solid-state drives. However, hard disk drives have their, you know, benefits, that [music] mostly being cheaper. I'm not going to go too deep into the differences between these. However, if anyone wants a video on building computers, I've been building them since I was a teenager, and I'm happy to make some cool ones on that as well. Just please comment below like, "Build me a computer or something like that." I don't know.
Anyway, the whole system is a trade-off between speed [music] and capacity. Fast memory is expensive, small, and cheap memory is slow but large. So we build hierarchies. We move data between levels based on how often it's accessed. We predict what we'll need and stage it closer to the processor based on those predictions.
But here's the part that matters for what comes next. In all of this, there is a hard boundary between code and data. The instructions that tell the machine what to do are kept separate from the information this machine operates on. In fact, mixing them is considered a security vulnerability. It's how buffer overflow attacks work. You trick the machine into treating data as code. The separation is seemingly foundational. It's baked into the architecture until it isn't.
When people talk about AI memory, they usually mean one of two things. Either they mean the model's training, the patterns baked into the weights through its exposure to data, or they mean the context window, the text the model can see during a single conversation. But if you're actually building with these systems, you know there's a lot more going on. And for those of you who don't, there's a hierarchy here, too. And it rhymes with the one we just talked about, but the differences really do matter.
At the base, you have the model weights. This is the closest analog to ROM, read-only memory. It's fixed at training time. It encodes patterns, knowledge, capabilities. You can't change it during inference. It's just what the model knows. From there, you can look at another part called the context window. This is more like working memory. It's everything the model can attend to when generating a response. The system prompt, its conversation history, any documents or data that have been included. It's temporary and it exists only for this interaction. When the conversation ends, it's gone.
Now, the rest of these memories are kind of stored within all of those two. In that context, you have a system prompt functions something like a firmware, almost. It sets the operating parameters. It tells the model what it is, who it is, what it should do, what constraints to follow, and often the user, or an outside user, typically doesn't see it, but it does shape everything in the conversation or process. Then you have retrieved context: things that are pulled dynamically based on a query, a relevant document, or a skill file that contains specialized instructions. This is like loading from a disk into RAM. It happens on demand. However, there's another version of this that is still the same, but I think is important to separate. It's called persistent memory. And this is information stored between conversations and selectively loaded when relevant. Almost like summaries. It's not everything, just what seems useful for every single conversation. And it's retrieved in the same way as the previous method, but just is a slightly different concept around it.
But if you squint, this looks like a traditional hierarchy. Speed versus capacity trade-offs, data moving between levels, locality patterns [music] determining what gets loaded. But there is a difference, and it's not a small one. In this system with AI, code and data end up being the same thing. The system prompt is not compiled separately from the user's message. The skill file is not executed through a different pathway than the conversation history. They are all just text. All processed identically, all tokens in a sequence that the model attends to and continues.
When you write an instruction in a system prompt, you're not giving the model a command that it executes. You're adding words to a document that the model will read and then continue. The instructions become part of the context. Reading it is the execution. And this is why prompt injection works. If someone puts text in a document that says "ignore your previous instructions," and that document gets loaded into the context, the model reads those [music] words the same way it reads everything else. There is no separate layer that validates code versus data. There is no boundary to cross. It's all just text.
But this is not a bug. It is the architecture. And when I say that, I think it's important for us to go back in time. Back into 1952, Grace Hopper had a working compiler. It could take instructions written in something closer to English and translate them into machine code. However, nobody believed her. She said, "I had a running compiler and nobody would touch it. They told me computers could only do arithmetic." She spent years trying to convince people that her compiler was truly possible, or at least this type of process and abstraction. But she said people were allergic to change.
Now, the skepticism came from a genuine place. Compilers did arithmetic. That was the foundational truth. However, a compiler seemed to violate that truth by claiming computers could process language. However, Hopper wasn't claiming that computers had stopped doing arithmetic. She had simply built a layer on top of that that translated human-readable instructions into the arithmetic the machine already understood. The machine still did what it always did. The abstractions just made it accessible to more people in new ways.
And this pattern repeated. Assembly gave way to higher-level languages. Those gave way to interpreted languages. Each time, skeptics pointed to the layer below and said, "This is what's really happening, and you're adding unreliability by abstracting it." And each time, engineers built the infrastructure to make the new layer reliable enough to trust.
Now, I talked a whole bunch about this in a previous video. Please go check it out. But if you trace a single line of Python from all the way down through the interpreter, through the C code, through the assembly, through the machine code, through transistors, you eventually hit electrons, particles that at the quantum level are genuinely probabilistic, not deterministic. They don't have definite positions until measured. The entire supposedly deterministic computing stack is built on quantum uncertainty. We made it reliable through architecture, through error correction, through redundancy, through engineering that handles the failures we can't prevent.
When people say AI can't be trusted because it's probabilistic, they're not wrong at one level, but they're missing the pattern. Every layer of the stack was probabilistic or unreliable at first. Grace Hopper's compiler was unreliable until it wasn't. High-level languages were toys until they weren't. The reliability came from building the infrastructure, from doing the engineering work. That work is happening now. It just looks different because the abstraction layer is different.
And if it is different, let's go look at all this but from the perspective of prompting. And you see, most people think of, "Write me a poem. Be specific. Add examples. Use personas." Compared to what we were just looking at a little bit ago, right? This idea of memory. How is memory being used to move all these things around? Right? Most people just talk about tips [music] and tricks. And this is really just surface level. It's like telling someone that programming is about knowing which words to type, which technically it is, but it's much more than that. And I see a lot of engineers completely ignoring this difference, and people just getting into the AI world. Production looks completely different. We ask a lot of different questions: architectural design decisions, things like that, like how to structure memory files, when to retrieve additional information, how do we manage the interaction between system-level instructions and user input, how do we handle the fact that all this gets flattened into a single stream where code and data are indistinguishable? This is the same discipline. It's not anything different than the last 100, 200 years. It's what prompting looks like when you actually go deeper.
And you see, this is programming. It's at the highest abstraction layer we've built so far. [music] It's natural language as an interface and meaning as the mechanism. In prior lower languages, syntax was compiling into machine code. But now we're looking at context that shapes probability distributions over continuations. The Mad [music] Libs pattern template still holds. You're filling type slots in a structure you can't fully see, and the output depends on what you provide and [music] where it goes. The difference is what we're optimizing for. In Mad Libs, the wrong word is funny. In programming, the wrong word is a bug. In prompting, the wrong context is a hallucination, a jailbreak, a failure to understand what you actually need.
And we're [music] still in the early days of figuring out what the right abstractions are, what structures make this reliable, what architectural patterns let us trust the output. The way we eventually learned to trust compilers, learned to trust garbage collection, and all these other layers of abstraction between our code and the electrons underneath. Grace Hopper once said that the most dangerous phrase in the language is "we've always done it this way." We're at a moment where the patterns are repeating, but the medium looks different enough that it's easy to miss. The skepticism sounds reasonable. The concerns about reliability are real, but the path forward is the same path it's always been: build the infrastructure. Do the engineering. Make the unreliable thing reliable enough to trust. That is what prompting is, at this level at least. Not tips for talking to a chatbot, not tricks for getting better outputs. It's the next layer of the stack. Until next time, my friends, have a good day.