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 critical failures with legal contracts, API docs, 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 keyword search using Reciprocal Rank Fusion, then applied a cross-encoder reranker to narrow 50 candidates down to 5, adding only 50ms while improving recall by 15%. A Bayesian optimization layer was also introduced to tune retrieval parameters, collectively cutting end-to-end query latency by 40%. The team published their approach and code on DEV Community as a practical guide for engineering teams scaling RAG systems beyond demo environments.