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

Short summary

A practical deep-dive on rebuilding a RAG retrieval pipeline from first principles, 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 approach achieved 95% recall@10 and 40% latency reduction through a Bayesian search optimization, with concrete code and benchmark tables.

• •Document-type-specific chunking strategies outperform fixed 512-token splits
• •Hybrid retrieval (vector + BM25 + reranker) with RRF beats either method alone
• •Cross-encoder reranking costs 50ms but gains 15% recall over bi-encoder similarity

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