Volume 7
Volume 7 - Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation is the bridge between language models and trusted knowledge. This volume teaches RAG as a system, not a shortcut. The reader moves from the purpose of RAG to embeddings, chunking, vector databases, retrieval, hybrid search, re-ranking, metadata filtering, production architecture, evaluation, unsupported-claim controls, and enterprise knowledge bases.
Chapters
13
Lesson Reading
5 hr 15 min
Labs
13
Interview Sets
13
What This Volume Covers
Read the sections in order if you are building from scratch. Experienced readers can jump to a section, but the chapters are sequenced to build vocabulary, mental models, implementation judgment, and architecture readiness.