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 LLM-guided agentic chunking, achieving recall@10 scores between 91% and 97% across content types. They adopted a hybrid retrieval approach combining vector search and BM25 keyword matching, fused via Reciprocal Rank Fusion, followed by a cross-encoder reranking step that improved recall by 15%. The 50-to-5 candidate funnel added only 50 milliseconds of latency while significantly boosting relevance, contributing to an overall 40% reduction in end-to-end query latency. The team also introduced query transformation as a preprocessing step to compensate for poorly formed user queries before retrieval begins.