# Track 09: Retrieval-Augmented Generation (RAG)

Grounding an LLM's answers in your own documents instead of its training
data alone. Builds a naive RAG pipeline early, then spends the rest of
the track on everything that makes the difference between a demo and a
production system: parsing real documents, hybrid retrieval, reranking,
citations, and honest evaluation.

## Modules

1. Why RAG Exists
2. Naive RAG End to End
3. The Ingestion Pipeline
4. Document Loaders and Parsing
5. PDFs, Tables and Complex Documents
6. The Retrieval Step
7. Keyword Search and BM25
8. Hybrid Search
9. Query Rewriting and Expansion
10. Reranking With Cross-Encoders
11. Context Assembly and Prompt Construction
12. Citations and Source Attribution
13. Evaluating Retrieval Quality
14. Evaluating Generation Quality
15. RAGAS and Evaluation Frameworks
16. Advanced RAG: GraphRAG and Agentic RAG
17. RAG Failure Modes
18. Capstone Project