AI · Extended learning · 2026-10-10

RAG: retrieval-augmented generation and traceable answers

Understand retrieval, ranking, context assembly, citations, and failure strategies in Retrieval-Augmented Generation.

Concept notes

Retrieval-Augmented Generation (RAG) separates “finding evidence in source material” from “using the model to shape an answer.” A reliable flow usually includes document cleanup, chunking, embeddings, indexing, retrieval, reranking, context assembly, generation, and citation display. It does not guarantee correctness; if retrieval fails, the source is stale, permissions are wrong, or the prompt asks for too much inference, the model can still hallucinate. The learning goal is to make answers traceable, rejectable, and observable rather than merely fluent.

Tradeoffs and safety exercise

Paper/local safety exercise: pick one public document page and split it into three chunks by hand, then write a title and sourceUrl for each chunk. Ask a question that can be answered only by the second chunk, and then write the generation rule: use only the provided chunks; if there is no evidence, say you cannot confirm; and list citations after the answer. Then ask an out-of-scope question and check whether the system refuses. This exercise does not require a model call, but it still trains you to design evidence boundaries for RAG.

Completion checklist

• Can explain the main steps in a RAG pipeline.
• Can explain why citations are audit evidence, not decoration.
• Can design response rules for no-evidence, permission-denied, and stale-content cases.