How LLMs actually works?

Have you ever chatted with an AI and felt a shiver of awe? Like it wasn't just following rules, but actually getting what you were saying? Or watched as a simple prompt turned into a fully formed story, complete with characters and a plot? It's easy to think of these large language models, or LLMs, as pure magic, but the reality is even cooler—it's a symphony of mathematics and data that's both mind-bending and surprisingly elegant.

Let's pull back the curtain and peek inside the "brain" of one of these digital wizards. It's a bit like learning how a master painter works: you see the finished canvas, but the real genius is in the strokes, the colors, and the technique.

Step 1: Turning Words into a Language the AI Understands

A computer doesn't see "cat" or "house" like we do. It sees numbers. So, the first thing an LLM does is translate our words into its own numerical language.

1. Breaking It Down: Your sentence, "The dog chased the ball," isn't seen as a single unit. It's broken into individual pieces, or "tokens." Sometimes a token is a whole word, like "dog," and sometimes it's a part of a word, like "un-" or "-ing."

2. Giving Words a "Feeling": Each of these tokens is then converted into a list of numbers, called a "vector." This isn't just a random list. Think of it like a coordinate on a super-dimensional map. During training, the model learns to place similar words near each other on this map. So, the vector for "king" will be close to "prince," and the vector for "happy" will be near "joyful." This is how the AI starts to grasp the meaning of a word.

So, when you type a sentence, the LLM is actually looking at a series of number vectors, each one holding the "essence" of a word.

Step 2: The Core of the Magic: The Transformer Block

This is where things get really clever. At the heart of most modern LLMs is a revolutionary structure called the Transformer Block. Imagine a group of friends chatting. Each person is listening to the others, but they're also figuring out who to pay the most attention to. That's what the Transformer Block does, but for words.

The "Attention" Mechanism: Who's Important Here?

For every single word in your sentence, the model asks: "Which other words are most important for me to understand my meaning right now?"

• The Three "Roles": Every word is given three jobs: a Query (what am I looking for?), a Key (what information do I have?), and a Value (here's my information!).

• The Connection: The model compares the Query of one word with the Key of every other word. The more they match, the higher the "attention score."

• A New Perspective: These attention scores are used to create a new, refined version of the word's number vector. This new vector is no longer just the word's meaning in isolation; it now holds a blend of all the other words' meanings, weighted by how important they are.

This is why an AI understands the difference between "The bank of the river" and "The money in the bank." The attention mechanism in the first phrase will heavily weigh "river," while in the second, it will focus on "money." This parallel processing is what makes modern AI so fast and powerful. It’s not just reading word-by-word; it's looking at the whole sentence at once.

Step 3: Layer Upon Layer of Understanding

One "attention" step isn't enough. The AI runs the contextualized words through another layer of processing, and then another, and another. Modern LLMs can have dozens or even hundreds of these layers stacked on top of each other. Each layer builds on the last, gradually refining the understanding of the text, from simple word relationships to complex, abstract ideas.

Step 4: The Final Act: Picking the Next Word

After all this deep, multi-layered processing, the model is left with a final set of number vectors. Now, it's time for the grand finale. The AI's only job is to do one thing: predict the next most likely word. It looks at the final vectors and calculates the probability of every single word in its vocabulary. The word with the highest probability is chosen and added to the sentence.

Then, the entire process starts over with the new, longer sentence. It's a continuous, step-by-step prediction game, building a sentence, a paragraph, or an entire article one word at a time.

The Big Picture: How They Learn

An LLM isn't explicitly taught grammar or facts. It learns by playing this prediction game on a colossal scale. It's fed mind-boggling amounts of text from the internet, books, articles—everything. It sees "I am hungry for a good __," and it tries to predict "meal." If it gets it wrong, it subtly adjusts its internal settings to be more likely to get it right next time.

This simple, repetitive task, repeated trillions of times, is what allows these models to develop an uncanny grasp of grammar, reasoning, and even creativity. It's an example of how a very simple rule, applied on a massive scale, can lead to astonishing complexity.

So, the next time you see an AI write a poem or summarize a document, remember that it's not magic. It's a sophisticated, beautiful dance of numbers and probabilities, all working in harmony to predict the next word—and in doing so, creating a world of possibilities.