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 revealed significant performance gaps with legal, API, and support ticket documents. The team replaced fixed-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 implemented a hybrid retrieval system combining vector search and BM25 keyword search, fused via Reciprocal Rank Fusion, followed by a cross-encoder reranking stage that improved relevance correlation from 0.75 to 0.92. Query transformation was also introduced to handle poorly formed user queries before retrieval. The combined changes resulted in a 40% reduction in end-to-end query latency compared to the original pipeline.