Your foundation for everything AI. From neural networks to prompt engineering, start here.
Strip the hype. Learn what an LLM actually does, token by token.
Pre-training, instruction tuning, alignment — and what each one means for your choices.
The mechanics of context — and how to reason about fit, cost, and truncation.
Walk through structuring a prompt that gets consistent, production-quality output.
The sampling parameters that shape creativity, determinism, and diversity.
A practical framework for identifying where AI adds value and where it doesn't.
How to know if a model's answer is actually good, beyond vibes.
A map of frontier labs, open models, and the trade-offs between them.
