Large language models (LLMs) feel complex, but you can think of them as a simple pipeline:
Tokenization chops text into small units (“tokens”)
Embeddings turn those tokens into numbers that capture rough meaning.
Positional encoding injects word order so the model knows “who came first.”
Attention lets the model look across all tokens and weigh what matters most in context.
Transformer stacks many layers of attention + small neural nets to refine understanding.
You've probably chatted with ChatGPT, asked Claude a question, or watched an AI write an email. It seems like magic—how does a computer understand what you're saying and respond so naturally? The truth is both simpler and more amazing than you might think.
Large Language Models (LLMs) aren't actually "thinking" like humans do. Instead, they're like an incredibly sophisticated assembly line that breaks down your words, processes them through several stages, and builds up a response piece by piece. Let's take a journey through this fascinating process.
Imagine you're teaching a friend who speaks a completely different language to understand English. The first challenge? They need to know where one word ends and another begins. That's exactly what tokenization does for computers.
When you type "Hello, how are you today?" the computer doesn't naturally understand this as separate words. It sees just a string of characters. Tokenization is like having a smart assistant that chops up your sentence into bite-sized pieces the computer can work with.
But here's where it gets interesting: these pieces aren't always complete words. Sometimes the computer breaks "unhappy" into "un-" and "happy" because it has learned that "un-" often means the opposite of whatever comes after it. This is like learning that if you understand "happy," you can figure out "unhappy," "unkind," and "unfair" even if you've never seen those exact words before.
Each of these pieces gets assigned a number, like items getting barcodes in a store. So "Hello, how are you today?" becomes a string of numbers that the computer can actually work with.
Now we have a bigger challenge. The computer has numbers representing words, but numbers alone don't mean anything. The number 47 for "cat" and number 1,293 for "dog" don't tell the computer that cats and dogs are both pets, both animals, both furry.
This is where something beautiful happens. The computer creates what's like a massive, invisible map where every word gets its own location. Words with similar meanings get placed close together on this map, while unrelated words are far apart.
Think of it like organizing a huge library. You wouldn't randomly scatter books everywhere—you'd put all the cookbooks in one section, all the mysteries in another. On the computer's meaning map, "happy," "joyful," and "cheerful" are neighbors, while "happy" and "carburetor" are in completely different neighborhoods.
The amazing part? The computer learns to build this map just by reading lots of text and noticing which words appear in similar situations. It figures out that "dog" and "puppy" are related because they both appear after words like "cute" and "playful."
Here's a problem: our meaning map tells us what words mean, but it doesn't tell us what order they came in. Yet order matters enormously! "The dog chased the cat" means something very different from "The cat chased the dog."
The computer solves this by adding a kind of "timestamp" to each word—a mathematical signature that says "I'm the first word," "I'm the second word," and so on. It's like numbering the pages of a book so you know the correct order even if they get shuffled.
This positioning information gets mixed right into the word meanings, so the computer always knows both what each word means and where it appeared in your sentence.
Now comes perhaps the most clever part. When you're having a conversation, you naturally know what words relate to what. If someone says, "I bought a car yesterday. It was expensive," you automatically know "it" refers to the car, not to yesterday.
The attention mechanism gives computers this same ability. When the computer encounters the word "it," it can look back through everything that came before and figure out what "it" most likely refers to. It's like having a spotlight that can shine on the most important parts of the conversation.
But it's even smarter than that. The computer actually has multiple spotlights working at once—one might focus on connecting nouns and verbs, another might track what pronouns refer to, and a third might follow the emotional tone of the conversation.
When processing any word, these attention spotlights scan through all the previous words and decide which ones are most important for understanding the current word in context.
All these pieces—the word meanings, the positions, the attention mechanisms—get combined in what's called a transformer. But one layer isn't enough for complex understanding, so computers stack dozens or even hundreds of these layers on top of each other.
Think of it like learning to read. First, you learn individual letters, then you combine letters into words, then words into sentences, then sentences into paragraphs, and finally you can understand complex stories and ideas. Each transformer layer is like moving up one level of understanding.
The first few layers might just figure out basic grammar—which words go together, what's a noun versus a verb. The middle layers start understanding meaning—recognizing that "automobile" and "car" are the same thing. The final layers can handle complex reasoning, like understanding sarcasm or following a logical argument.
Modern AI systems like GPT-3 have 96 of these layers and 175 billion individual adjustable settings (called parameters). That's like having a library with 175 billion books, each containing a tiny piece of knowledge about language and the world.
How does the computer learn all this? Through a surprisingly simple game: predict the next word. Give the computer "The cat sat on the..." and it tries to guess "mat" or "floor" or "couch." It does this billions of times with text from books, websites, and articles.
Every time it guesses wrong, it adjusts those 175 billion settings just a tiny bit. After seeing enough examples, it gets remarkably good at predicting what comes next. But in learning to predict the next word, something amazing happens—it accidentally learns grammar, facts about the world, how to reason, and even how to have conversations.
It's like learning to speak by listening to millions of conversations. Eventually, you don't just learn the words—you learn how people think and communicate.
When you type a message to ChatGPT, this entire process happens in milliseconds. Your words get chopped up, converted to the meaning map, stamped with position information, then flow through dozens of layers that build up understanding and decide what to say next.
The computer generates one word at a time, always asking "Given everything said so far, what's the most helpful word to say next?" It repeats this process for each word until it has a complete response.
What's most amazing is that this system—built just to predict the next word—somehow learns to write poetry, explain science, help with problems, and have natural conversations. Nobody programmed it to be helpful or creative or knowledgeable about history. It learned all that just from trying to predict what comes next in human text.
It's like teaching someone to be a great conversationalist just by having them fill in the blanks in millions of conversations. Somehow, from this simple task, they learn not just words but wisdom.
Of course, these systems aren't perfect. They can confidently state things that aren't true, they might struggle with brand-new information, and they can reflect biases from their training data. They're incredibly sophisticated pattern-matching systems, not truly thinking beings.
But understanding how they work helps us use them better and appreciate both their capabilities and limitations. The next time you chat with an AI, you'll know about the incredible journey your words take—from simple text through meaning maps, attention mechanisms, and layers of understanding, all to generate a response that feels remarkably human.
The magic isn't that computers have learned to think like us—it's that we've figured out how to teach them to communicate with us using the patterns hidden in billions of human conversations.