Gen AI & Solutions Engineer
How Do Large Language Models Actually Work?
Large language models (LLMs) like ChatGPT, Claude, and LLaMA seem to do it all — write stories, solve math problems, summarize reports, debug code. But how do they actually work? You've probably heard phrases like "transformer model" or "trained on the internet," but what's really going on under the hood?
This post gives you a technical—but accessible—overview of how LLMs are trained, how they generate responses, and why they feel so smart (even though they're just predicting text).
At the Core: Just Predicting the Next Token
LLMs are fundamentally next-token predictors. That means they're trained to guess what comes next in a string of text, over and over and over.
Example:
"The capital of France is" → Predict: " Paris"Text is broken down into tokens — subword units or characters — and each token is converted into a vector (a list of numbers). From there, the model learns patterns and probabilities in language. The model's goal: minimize the difference between its predicted token and the actual one from the training data.
That's it. But when you scale it up across billions of examples and hundreds of billions of parameters, the result feels like intelligence.
How LLMs Are Trained: 3 Crucial Phases
LLMs go through three major training phases, each adding an essential layer of capability.
1. Pretraining: Learn from the World
Goal: Learn general language structure and facts. In pretraining, the model ingests massive datasets: books, websites, Wikipedia, open-source code, and more. It's trained on trillions of tokens using a technique called causal language modeling — predicting the next token, one after another.
The model learns:
- Grammar and syntax
- Knowledge about the world
- Patterns in code, dialogue, and reasoning
But it doesn't yet know how to follow instructions. It's like a very smart parrot with no steering wheel.
2. Fine-Tuning: Specialize the Model
Goal: Make the model good at a specific domain or task. Once pretrained, a model can be fine-tuned using smaller, curated datasets.
Examples:
- Fine-tune on legal documents → legal assistant
- Fine-tune on customer support transcripts → enterprise chatbot
This helps the model perform better in a narrow domain but doesn't necessarily make it better at talking to humans.
3. Instruction Tuning: Teach It to Follow You
Goal: Make the model helpful, safe, and steerable. Instruction tuning (sometimes combined with Reinforcement Learning from Human Feedback, or RLHF) trains the model on examples of how to respond to user prompts.
These are structured like:
Prompt: "Summarize this email."
Response: "Here's a summary..."This is what turns the raw model into something like ChatGPT — a conversational assistant that can actually follow directions and act polite, helpful, and aligned with user intent.
Under the Hood: The Transformer Architecture
The engine that powers all of this is called a Transformer, introduced in the now-famous 2017 paper Attention Is All You Need.
Here's what makes transformers special:
Self-Attention
Instead of reading text one word at a time, transformers can look at all the tokens in a sequence at once, and learn which ones are most relevant to each other.
This is how models understand context like:
In "The cat sat on the mat," "cat" and "sat" are strongly related.
Stacking Layers
Transformers consist of dozens (or hundreds) of layers that build more and more abstract representations of the input. The final layers don't just "read" language — they start to model relationships, infer structure, and even simulate reasoning.
Text Generation: One Token at a Time
Once trained, the model generates text like this:
- You type: "Tell me a joke about bananas."
- The model turns your prompt into vectors.
- It predicts the next token — maybe "Why".
- Then the next — " did".
- And so on — " the banana go to the doctor?"
It does this one token at a time, each prediction based on everything it's generated so far. This feels conversational and smooth, but it's really just high-speed next-token guessing.
But How Does It "Know" Things?
It doesn't — not in the way humans do. LLMs have no memory, belief, or intent. But because they've seen so much text, they learn that:
"The capital of France is" → is usually followed by "Paris".
They're statistical engines, not thinkers. But since human language follows patterns, that's often enough to create the illusion of understanding.
Why They Feel Smart Anyway
LLMs can:
- Recall factual knowledge (with some gaps)
- Mimic reasoning patterns (math, logic, code)
- Adapt to tone and style (e.g., write like Shakespeare or Python)
But they still:
- Make up facts (hallucinate)
- Struggle with math precision
- Don't know what they don't know
Add-ons: What Makes Modern LLM Apps Powerful
Today's most useful AI products combine LLMs with tooling and context injection, including:
| Technique | What it Does |
|---|---|
| RAG (Retrieval-Augmented Generation) | Adds external knowledge (e.g., from PDFs or databases) at runtime |
| Agents | Let the model plan steps, call tools, and reason iteratively |
| APIs & Tool Use | Let models call calculators, databases, or search engines |
| Memory & Context Windows | Helps models stay coherent in long chats |
Final Thoughts
Large language models are incredible tools — not because they understand the world, but because they've gotten very good at predicting how we talk about it. Through pretraining, fine-tuning, and instruction tuning, we take a raw statistical engine and shape it into something helpful, insightful, and humanlike. There's no magic here. Just brilliant math, massive data, and careful training.
And yet… the results do feel magical.