Lesson 10Giving the Model a Library and a Past
Welcome to Module 6: RAG & Memory — the course's pivot point. Modules 1–5 were about the model; from here on, it's about the system around the model. And this module cashes a promise made all the way back in Topic 12: "fine-tuning for form, RAG for facts." Facts finally get their architecture.
One reassurance before we start: you already own the hard parts. Embeddings (Topic 6), the whiteboard (Topic 5), grounding as anti-hallucination layer 2 (Topic 21), even a working semantic search engine (your Lesson 2 exercise — that 10×10 similarity grid). This module assembles pieces you've held for weeks.
In this lesson
- 48RAGthe open-book examRAG (Retrieval-Augmented Generation) answers the question every product hits in week one: how does the model know about MY data? Your docs,…3 min
- 49Chunkingthe cut decides the qualityChunking looks like a preprocessing footnote. It is, in practice, the highest-leverage decision in most RAG systems — more than the vector…3 min
- 50Vector Databasessearch at scaleYour Topic 48 exercise compared a query against 30 vectors by brute force — checking every single one. Fine at 30. At 10 million chunks ×…4 min
- 51Semantic Searchwhat embeddings miss, and the hybrid fixSemantic search — embed query, find nearest chunks — has been our quiet workhorse since Lesson 2. Time to be honest about where it fails,…3 min
- 52Retrieval Pipelinesfrom demo to productionNaive RAG — embed, top-5, stuff, pray — demos beautifully and plateaus at "mediocre with confidence." Production quality comes from…4 min
- 53AI Memory Systemsthe goldfish gets a pastClose your eyes and recall Topic 5: the model is stateless — a goldfish handed a transcript each time — and "memory" in chat products is…5 min
Start with Topic 48: RAG, or browse all 90 topics.