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 fixed-token chunking and basic vector search proved inadequate in production environments. The team adopted document-type-specific chunking strategies — including recursive, semantic, and agentic methods — achieving recall@10 scores between 91% and 97% across legal, API, and support content. They replaced pure vector search with a hybrid retrieval system combining vector search, BM25, and cross-encoder reranking, which improved relevance correlation from roughly 0.75 to 0.92. A Bayesian search optimization was also introduced, collectively reducing end-to-end query latency by 40% compared to the original pipeline. The team published their approach, including code samples and configuration tables, as a practical guide for engineers scaling RAG systems beyond demo-stage performance.