Understanding Large Language Models: How ChatGPT and Claude Actually Work
Understanding Large Language Models: How ChatGPT and Claude Actually Work
Abstract neural network visualization
You have probably used ChatGPT or Claude by now. You type a question, and seconds later you get a coherent, often surprisingly insightful answer. But how does it actually work? Is the AI "thinking"? Does it "understand" your question?
The truth is both simpler and more fascinating than most people realize. In this article, we break down the technology behind large language models (LLMs) in plain English -- no PhD required.
What Is a Large Language Model?
A large language model is a type of artificial intelligence that has been trained on massive amounts of text to predict what words should come next in a sequence.
That is the core idea. Everything else -- the conversations, the code generation, the creative writing -- emerges from this deceptively simple objective: predict the next word.
Here is a simplified example:
Input: "The cat sat on the ___"
Model prediction: "mat" (with high probability)
But modern LLMs do not just fill in single blanks. They generate entire paragraphs, essays, and code by predicting one token at a time, thousands of times in sequence.
What Are Tokens?
Before an LLM can process text, it needs to break it down into tokens. Tokens are the smallest units the model works with.
- •The word "hello" is one token
- •The word "understanding" might be split into "under" + "standing" (two tokens)
- •A space before a word is often included in the token
- •Numbers, punctuation, and special characters are separate tokens
Why tokens matter:
- •They determine the model's "context window" -- how much text it can consider at once
- •GPT-4 has a context window of 128,000 tokens (roughly 100,000 words)
- •Claude can handle up to 200,000 tokens in some configurations
- •Longer context = better understanding of your full conversation
Fun fact: The average English word is about 1.3 tokens. So when a model says it supports 100K tokens, that is roughly 75,000 words -- about the length of a full novel.
The Transformer Architecture
The breakthrough technology behind every modern LLM is the Transformer, introduced in a landmark 2017 paper by Google researchers titled "Attention Is All You Need".
Before Transformers, AI language models processed text sequentially -- one word at a time, left to right. This was slow and made it hard to capture long-range relationships in text.
Transformers changed everything by introducing parallel processing and a mechanism called attention.
How Transformers Process Text
- 1Tokenization - The input text is split into tokens
- 2Embedding - Each token is converted into a numerical vector (a list of numbers that represents its meaning)
- 3Positional Encoding - The model adds information about where each token appears in the sequence
- 4Attention Layers - The model figures out which tokens are most relevant to each other
- 5Feed-Forward Layers - The model processes these relationships to build understanding
- 6Output - The model predicts the probability of each possible next token
This entire process happens through dozens (sometimes over a hundred) layers of computation, each refining the model's understanding.
The Attention Mechanism (The Secret Sauce)
Attention is what makes Transformers so powerful. It allows the model to look at every other token in the input when processing any single token.
Consider this sentence:
"The animal didn't cross the street because it was too tired."
What does "it" refer to? The animal or the street? You immediately know it is "the animal" because animals get tired, streets do not. The attention mechanism gives the model this same ability -- it learns to connect "it" with "animal" by assigning a high attention weight between those tokens.
Self-Attention in Practice
For each token, the model computes three things:
- •Query - "What am I looking for?"
- •Key - "What do I contain?"
- •Value - "What information do I provide?"
The model compares each Query against all Keys to determine which tokens are most relevant, then combines the corresponding Values. This is computed in parallel for all tokens simultaneously, which is why Transformers are so fast.
Multi-head attention means the model runs multiple attention computations in parallel, each focusing on different types of relationships (grammar, meaning, context, etc.).
How LLMs Are Trained
Training a large language model is a multi-stage process that requires enormous computational resources.
Stage 1: Pre-training
The model reads trillions of tokens from the internet -- books, articles, websites, code repositories, forums, and more. During this phase, the model learns to predict the next token by adjusting billions of internal parameters.
- •GPT-4 has an estimated 1.7 trillion parameters
- •Claude by Anthropic has not disclosed its exact parameter count
- •Llama by Meta ranges from 7 billion to 405 billion parameters
> What are parameters? Think of them as the model's "knowledge knobs." Each parameter is a number that gets adjusted during training. Together, billions of parameters encode patterns about language, facts, reasoning, and more.
Stage 2: Fine-Tuning
After pre-training, the model is good at predicting text but not great at following instructions or having conversations. Fine-tuning teaches it to be helpful.
- •Supervised Fine-Tuning (SFT) - Human trainers write example conversations showing how the model should respond
- •RLHF (Reinforcement Learning from Human Feedback) - Humans rank multiple model outputs from best to worst, and the model learns to prefer better responses
- •Constitutional AI (Anthropic's approach for Claude) - The model is trained to follow a set of principles, reducing the need for human ranking
Stage 3: Safety and Alignment
This is where companies ensure the model does not produce harmful, biased, or misleading content. Different companies take different approaches:
- •OpenAI uses RLHF and content filters
- •Anthropic uses Constitutional AI and extensive red-teaming
- •Google uses a combination of RLHF and rule-based filters
- •Meta relies on community feedback for their open-source models
What LLMs Can and Cannot Do
Understanding the limitations is just as important as understanding the capabilities.
What They CAN Do
- •Generate coherent, contextual text in multiple languages
- •Write, explain, and debug code in dozens of programming languages
- •Summarize long documents and extract key information
- •Answer factual questions (with caveats)
- •Translate between languages
- •Creative writing, brainstorming, and ideation
What They CANNOT Do
- •Access the internet in real-time (unless given tools to do so)
- •Guarantee factual accuracy - they can "hallucinate" plausible-sounding but incorrect information
- •Truly understand in the way humans do - they are pattern matchers, not conscious beings
- •Learn from your conversations (each session starts fresh unless explicitly designed otherwise)
- •Do math reliably beyond basic arithmetic (though this is improving rapidly with tool use)
The Difference Between GPT, Claude, Llama, and Gemini
All modern LLMs use the Transformer architecture, but they differ in training data, fine-tuning approach, and design philosophy:
| Model | Company | Open Source | Strength |
|---|---|---|---|
| GPT-4 / GPT-4o | OpenAI | No | General purpose, multimodal |
| Claude | Anthropic | No | Safety, long context, nuance |
| Llama 3 | Meta | Yes | Open source, customizable |
| Gemini | No | Multimodal, integrated with Google | |
| Mistral | Mistral AI | Partially | Efficient, strong for its size |
Why This Matters for You
Understanding how LLMs work helps you use them more effectively:
- 1Write better prompts - Knowing the model predicts tokens helps you craft prompts that guide it toward the output you want
- 2Recognize limitations - You will know when to trust the output and when to verify
- 3Stay informed - AI is reshaping every industry. Understanding the basics keeps you ahead
- 4Build with AI - If you are a developer, understanding LLMs is becoming as fundamental as understanding databases
Want to explore AI tools hands-on? Our AI Hub curates the best free AI tools you can try right now. And if you are a developer working with AI APIs, our JSON Formatter is invaluable for inspecting API responses from LLM providers.
Further Reading and Resources
- •Attention Is All You Need (original paper)
- •OpenAI's GPT-4 Technical Report
- •Anthropic's Constitutional AI Paper
- •3Blue1Brown's Neural Network series on YouTube - Excellent visual explanations
- •Andrej Karpathy's YouTube channel - Deep dives from a former OpenAI researcher
The field moves fast. What we covered today is the foundation, but new breakthroughs happen monthly. Stay curious, keep experimenting, and check our blog for more deep dives into AI and technology.