RAG Foundations in Retrieval-Augmented Generation

Lessons in this section

1. RAG Basics

   What Retrieval-Augmented Generation is and why it matters: retrieve, augment, generate; RAG vs fine-tuning vs long context; the end-to-end architecture; grounding, fewer hallucinations, fresh data, citations and real use cases.

2. RAG Pipeline Architecture

   The four RAG stages and how data flows between them: chunking, embedding, retrieval and generation; query processing, candidate selection, context packing and citation validation; determinism and the three runtime tiers.

3. Corpus Engineering

   Choosing and cleaning the documents a RAG system searches: document and ticket corpora, core set vs outer volume, near-duplicates, outdated contradictions, unanswerable questions, frozen snapshots, gold sets and reference chunks.