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

A development team overhauled their Retrieval-Augmented Generation (RAG) pipeline after standard fixed-token chunking and basic vector search proved inadequate in production environments. The team replaced uniform 512-token chunking with document-specific strategies — including recursive, semantic, and agentic chunking — tailored to content types such as legal contracts, API docs, and support tickets. They also implemented a hybrid retrieval system combining vector search, BM25 keyword matching, and cross-encoder reranking, which improved recall@10 to 95% across most document categories. A query transformation layer was added to handle poorly phrased user queries before retrieval begins. The combined changes reduced end-to-end query latency by 40%, bringing response times down from over one second to a significantly faster pipeline.