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 vector search proved inadequate in production environments. They 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. The team 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 optimization layer was applied to tune retrieval hyperparameters, collectively reducing end-to-end query latency by 40%. The redesigned pipeline replaced a demo-oriented 'chunk and hope' approach with a measurable, tunable system suited for large-scale production workloads.