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 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 combined vector search with BM25 keyword search using Reciprocal Rank Fusion, then added a cross-encoder reranking stage that improved recall by 15 percentage points at a cost of only 50ms. The optimized pipeline achieved up to 97% recall@10 on internal wiki content and reduced overall query latency by 40%. Query transformation was also introduced to handle poorly phrased user inputs before retrieval begins.