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 keyword search using Reciprocal Rank Fusion, then applied a cross-encoder reranker to narrow 50 candidates down to 5, adding only 50ms of latency while improving recall by 15%. A Bayesian search optimization layer further reduced overall query latency by 40%, bringing end-to-end response times well below the original one-second threshold. The team published their architecture and Python code to help other engineers move beyond demo-grade RAG setups toward measurable, tunable retrieval pipelines.