P8 taught you Agentic RAG — agents that control retrieval dynamically. This lesson goes deeper into the retrieval engine itself: hybrid search, chunking strategies, query understanding, reranking, and the metrics that tell you whether your RAG actually works.

SDK focusMCP toolsoutputFormatstructured evaluationmulti-step retrieval

The retrieval quality gap

Most RAG demos use a single vector search with default chunking. Production RAG demands more:

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Demo RAG:
  Document → Fixed-size chunks → Embed → Single vector search → Generate
  Problem: quality is unknown until measured on representative labeled queries

Industrial RAG:
  Document → Intelligent chunking → Hybrid index (BM25 + Vector)
  Query → Understand → Rewrite → Hybrid search → Rerank → Generate
  Result: Significantly higher retrieval accuracy with measurable metrics

The gap between demo and production RAG comes down to three areas: how you chunk, how you search, and how you measure.

Chunking strategies

Chunking is the most underrated part of RAG. Bad chunks mean bad retrieval — no amount of fancy search can fix garbage input.

Fixed-size chunking

The simplest approach. Split by token count with overlap: