Engineering Team Cuts RAG Pipeline Latency 40% With Bayesian Search and Hybrid Retrieval

A development team rebuilt their Retrieval-Augmented Generation (RAG) pipeline from scratch after standard production deployments revealed significant performance gaps, including latency exceeding one second per query. The team replaced fixed 512-token chunking with document-type-specific strategies, including recursive, semantic, and agentic chunking, achieving recall@10 scores between 91% and 97% across different content types. They combined vector search with BM25 keyword search using Reciprocal Rank Fusion, then applied a cross-encoder reranker to narrow 50 candidate results down to 5, adding only 50ms of latency while improving recall by 15%. A Bayesian-informed search optimization approach was also introduced to tune retrieval parameters systematically, ultimately reducing overall pipeline latency by 40%. The team shared code implementations and a production configuration table to help other engineers replicate the approach across legal, API documentation, support ticket, and internal wiki use cases.