How LLMs Actually Work
You cannot build reliable AI products without understanding why LLMs behave the way they do. Why does temperature matter? Why does prompt length affect cost? Why do models confidently make things up? Why does the same prompt produce different results tomorrow? This course builds the foundational mental model — not the mathematics, but the intuition — that separates builders who fight their models from builders who work with them.
What you'll learn
Course outline
Free — no account needed
What Is a Language Model
From autocomplete to reasoning — how predicting the next token gives rise to intelligence
Tokens and Context Windows
Why prompt length costs money, why long documents get summarised badly, and how to think in tokens
Temperature and Sampling
What temperature actually controls, why 0 is not deterministic, and when to use each setting
Full course — $29 one-time
How Training Works
Pretraining, RLHF, fine-tuning, and why the same base model can behave very differently
The Major Models Compared
Claude vs GPT-4o vs Gemini vs Llama — beyond the benchmarks to real-world builder trade-offs
System Prompts and Roles
How the conversation is actually structured under the hood — and how to use it effectively
Hallucinations and Why They Happen
The mechanism behind confabulation — and the architectural patterns that reliably reduce it
Choosing the Right Model
A practical framework for matching model capabilities to task requirements — cost, speed, quality
Get the full course
8 lessons — from tokens and attention to model selection, hallucination mitigation, and the model routing pattern.