AI Engineering

Short, self-contained lessons. Each one teaches a single idea tied to the mission: walk into a customer's problem and build a reliable solution on foundation models. Built on high-trust sources — Chip Huyen's AI Engineering, Andrej Karpathy's lectures, and Anthropic's engineering guides.

Lessons

• Lesson 01 · Foundations What an LLM Actually Is The next-token-predictor mental model — and how hallucination, non-determinism, amnesia, and prompt sensitivity all fall out of it.
• Lesson 02 · Foundations Tokens, Context & the Cost of a Call The three numbers behind every scoping conversation — plus your first hands-on lab: a real API call, the usage meter, and time-to-first-token.
• Lesson 03 · Core skills Prompt Engineering as an FDE Skill Up next Why wording moves the distribution, and the handful of techniques that reliably improve output quality.
• Planned Structured output & tool use · The agent loop · RAG · Evals · Fine-tuning (when it's worth it) The arc from mental models to building and proving customer-ready solutions.

Reference

• Living document Glossary The canonical vocabulary for the course — every lesson uses these terms exactly.