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

A development team overhauled their Retrieval-Augmented Generation (RAG) pipeline after standard production deployments proved inadequate for legal contracts, API docs, and customer support tickets. The team replaced fixed 512-token chunking with document-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 candidate results down to 5, adding only 50ms of latency while improving recall by 15%. A query transformation layer was also introduced to rewrite poorly formed user queries before retrieval. The cumulative changes resulted in a 40% reduction in end-to-end query latency and a measured retrieval pipeline with 95% recall@10.