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 critical failures in handling legal contracts, API docs, and customer support tickets. 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 content types. They combined vector search with BM25 keyword search using Reciprocal Rank Fusion, then applied a cross-encoder reranker to narrow 50 candidates down to 5, adding only 50ms of latency while improving recall by 15%. A query transformation layer was also introduced to handle poorly phrased user queries before retrieval. The cumulative changes resulted in a 40% reduction in end-to-end latency and a measured retrieval pipeline with 95% recall@10 at scale.