Production RAG Optimization: Chunking Strategies, Hybrid Retrieval, and Reranking for 95% Recall@10

Short summary

A detailed engineering walkthrough of rebuilding a RAG retrieval layer for production, covering chunking strategies by document type, hybrid retrieval combining vector search and BM25 with reciprocal rank fusion, and cross-encoder reranking. The author reports achieving 95% recall@10 and 40% latency reduction by tuning chunk size/overlap per document class and adding query transformation. Includes production config tables and Python code for each component.

• •Four chunking strategies (fixed, recursive, semantic, agentic) with per-document-type production configs and recall metrics
• •Hybrid retrieval using parallel vector + BM25 search, reciprocal rank fusion, and cross-encoder reranking (50→5 funnel)
• •Query transformation layer to handle poorly-formed user questions before retrieval

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