Optimizing RAG Retrieval: Chunking Strategies, Hybrid Search, and Reranking for 95% Recall@10

Short summary

A detailed engineering walkthrough of rebuilding a RAG retrieval layer for production, covering chunking strategies (fixed, recursive, semantic, agentic) tuned per document type, hybrid retrieval combining vector search and BM25 with reciprocal rank fusion, and cross-encoder reranking that improved recall@10 to 95% while cutting latency 40%. Includes Python code for all components and a production config table with measured recall metrics.

• •Four chunking strategies (fixed, recursive, semantic, agentic) with per-document-type production configs achieving 91-97% recall@10
• •Hybrid retrieval combining vector search and BM25 via reciprocal rank fusion, plus cross-encoder reranking for 15% recall gain
• •Query transformation step to handle poorly-formed user questions before retrieval

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