Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%

Short summary

A detailed technical walkthrough of rebuilding a RAG retrieval layer for production, covering document-type-specific chunking strategies, hybrid retrieval combining vector search and BM25 with reciprocal rank fusion, and cross-encoder reranking. Reports achieving 95% recall@10 and 40% latency reduction with concrete code examples and benchmark tables.

• •Document-type-aware chunking strategies with measured recall metrics
• •Hybrid retrieval combining vector search, BM25, and cross-encoder reranking via RRF
• •Query transformation layer to handle poorly-formed user questions

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