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

Short summary

A practical guide to building production RAG retrieval pipelines, covering chunking strategies by document type, hybrid retrieval combining vector search and BM25 with cross-encoder reranking, and query transformation. The approach achieved 95% recall@10 and cut latency by 40% through parallel retrieval and reciprocal rank fusion. Includes production config tables and Python code for each pipeline stage.

• •Document-type-specific chunking strategies (recursive, semantic, agentic) with measured recall@10
• •Hybrid retrieval via parallel vector + BM25 search with reciprocal rank fusion and cross-encoder reranking
• •Query transformation and Bayesian optimization reduced latency 40% while hitting 95% recall@10

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