01
What RAG is, and why
Why a model needs outside knowledge, what one RAG request does, and when RAG is the wrong tool.
Concept 1.1
Why models need outside knowledge, and what hallucination means
What a language model knows and does not know, why it answers confidently anyway, and why giving it your documents is the fix.
Concept 1.2
What RAG is: anatomy of a request
One question followed step by step through a RAG system, from the typed words to a cited answer, with what each step costs.
Concept 1.3
RAG vs fine tuning vs long context and prompt caching
Four ways to give a model your knowledge, what each one is good at, and what they cost per question on the same corpus.
Concept 1.4
Use cases and the quality, latency and cost triangle
Where RAG fits and where it does not, and how every setting trades answer quality against speed and cost, with top k as the measured example.
Chapter 1 recap