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10 · Retrieval-augmented generation (RAG)

10 · Retrieval-augmented generation (RAG)#

Chapter status: outline

This is the planned scope of Chapter 10. The complete lesson, executable examples, and solutions will be developed later. Chapter 1 is available now.

Question. How can a model answer using a specified collection of documents?

  • Learn: document chunking, indexing, retrieval, reranking, context assembly, citation, and abstention when evidence is insufficient.

  • Statistical lens: prediction conditional on selected evidence, retrieval error, measurement error, and the interaction between pipeline components.

  • Python practice: build a small pipeline over public statistical notes. Inspect retrieved passages, compare with a keyword baseline, and test answerable and unanswerable questions separately.

  • Self-check: identify whether an error arose in retrieval, evidence selection, or generation. A citation does not by itself show that its source supports the claim.

  • Reading: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.