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 caused latency exceeding one second per query and poor recall across document types. The team replaced fixed 512-token chunking with document-specific strategies — including recursive, semantic, and agentic chunking — achieving recall@10 rates between 91% and 97% depending on content type. 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 approach was used to tune retrieval weights, ultimately reducing end-to-end latency by 40%. The team published their methodology and code on DEV Community as a practical guide for engineers scaling RAG systems beyond demo environments.