intermediate~3h
Retrieval-Augmented Generation (RAG)
The single most consequential pattern in production LLM applications. This module builds it from first principles, then hardens it with pre- and post-retrieval techniques real systems actually need.
Learning objectives
- Beginner: A single VectorStoreDocumentRetriever with a fixed topK, over a small, clean document set — no pre/post-retrieval needed yet.
- Intermediate: Add a similarityThreshold to avoid injecting weakly-related passages just to fill topK, plus metadata filtering (Module 08 §5) for multi-tenant isolation.
- Advanced: Full pipeline: query rewriting (pre-retrieval), wide vector search, re-ranking (post-retrieval) down to the final few passages, combined with a RelevancyEvaluator (Module 16) verifying the final answer actually used the retrieved grounding rather than the model's own possibly-outdated training knowledge.
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Retrieval-Augmented Generation (RAG)