Introduction: How LLMs Work
Understand the fundamental mechanics of large language models so you can reason about real-world LLM systems
Learning goals
- •Understand how transformer architecture enables text generation
- •Learn about next-token prediction and autoregressive generation
- •Recognize the limitations of LLMs regarding memory and understanding
The Transformer Architecture
Large Language Models (LLMs) are neural networks trained on massive amounts of text data to understand and generate human-like language. At their core, they work by predicting the next most likely token in a sequence.
Modern LLMs are built on the transformer architecture, introduced in the 2017 paper "Attention Is All You Need." The key innovation is the attention mechanism, which allows the model to weigh the relevance of different parts of the input when generating each output token.
When you send a prompt to an LLM, here's what happens:
- Tokenization: Your text is broken into tokens (words or word pieces)
- Embedding: Each token is converted to a numerical vector
- Processing: The vectors pass through multiple transformer layers
- Prediction: The model outputs probabilities for the next token
- Generation: A token is selected and the process repeats
Next-Token Prediction
LLMs are fundamentally autoregressive models. This means they generate text one token at a time, using all previous tokens as context. The model doesn't "understand" in the human sense—it predicts statistical patterns learned from training data.
For example, when you type "The capital of France is", the model predicts "Paris" with high probability because it has seen this pattern millions of times in training data.
Key Insight
The model has no memory between conversations. Each request starts fresh. What appears as "understanding" is actually sophisticated pattern matching across billions of parameters learned during training.
The model can generate code because it has learned the statistical patterns of how code is structured from millions of code examples.