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 documentation, 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 and cross-encoder reranking using Reciprocal Rank Fusion, with the reranking step alone improving recall by 15% at a cost of only 50 milliseconds. Query transformation was also introduced to handle poorly phrased user inputs before retrieval. The combined optimizations reduced end-to-end query latency by 40% while pushing overall pipeline recall@10 to 95%.