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

Short summary

A detailed technical breakdown of optimizing RAG pipelines at scale, 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 cut latency by 40% through parallel retrieval and a 50-to-5 reranking funnel. Query transformation via LLM expansion is also discussed.

• •Document-specific chunking strategies (recursive, semantic, agentic) improve recall over fixed-token chunking
• •Hybrid retrieval with BM25 + vector search + cross-encoder reranking achieves 95% recall@10
• •Parallel retrieval and reranking funnel cut latency by 40% compared to naive pipeline

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