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

Short summary

A production-focused guide to optimizing RAG pipelines, covering chunking strategies (recursive, semantic, agentic) tuned per 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 through query transformation and a Bayesian search approach. Includes concrete code examples and a comparison table of chunking configs by document type.

• •Chunking strategy should vary by document type: recursive for legal, semantic for tickets, agentic for wikis
• •Hybrid retrieval (vector + BM25 + cross-encoder rerank) via reciprocal rank fusion outperforms either method alone
• •Query transformation and a 50-to-5 rerank funnel cut latency 40% while reaching 95% recall@10

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