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 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 boosting recall by 15%. A Bayesian optimization layer was used to tune retrieval parameters, ultimately cutting end-to-end query latency by 40%. The rebuilt pipeline replaced what the team described as a 'semantic search and hope' approach with a measurable, tunable system suited for large-scale production use.