Work through the modules in order, or jump to any lesson.
A standalone technical module following Andrej Karpathy's framework: from raw training data and tokenization through the transformer architecture, pretraining, alignment, reasoning models, and the modern inference ecosystem.
The Raw Material: Pretraining Data
What LLMs are actually trained on, how quality filtering works, and why the training data shapes everything the model knows — and doesn't know.
Tokenization: How Text Becomes Numbers
Before a model reads a single word, it breaks everything into fragments called tokens. This shapes cost, context limits, and some of the model's strangest failure modes.
The Architecture: What a Transformer Does
The neural network design that made modern AI possible — explained through what it computes, not how it's built. Attention, layers, and why scale changes everything.
Pretraining: Teaching a Model to Predict
How a blank neural network becomes a world model by predicting the next token, billions of times. What a base model is — and crucially, what it isn't.
From Base Model to Assistant: SFT and RLHF
The base model predicts text. A second training stage — Supervised Fine-Tuning and Reinforcement Learning from Human Feedback — teaches it to be helpful, harmless, and honest. This is where the AI assistant you interact with actually comes from.
Reasoning Models: Thinking Before Answering
A newer training approach gives models a scratchpad — and dramatically changes what they can do. The AlphaGo connection, chain-of-thought, and when to reach for a reasoning model.
Running Models: The Inference Ecosystem
Closed vs. open models, running locally, evaluating quality, and staying current. A practical map of the landscape for anyone using or building with LLMs.
Get fluent with the two platforms lawyers actually use — every capability on both, and the judgment for when to use which.
Move from operating a tool to directing a capable collaborator whose work you verify and own.