Engineering team cuts RAG pipeline latency 40% using Bayesian search and hybrid retrieval

A development team rebuilt their retrieval-augmented generation (RAG) pipeline from scratch 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 and cross-encoder reranking using Reciprocal Rank Fusion, which improved relevance correlation from roughly 0.75 to 0.92. A Bayesian optimization layer was introduced to auto-tune retrieval hyperparameters, contributing to the overall 40% reduction in query latency. The team shared full implementation details and benchmarks on DEV Community to help other engineers replicate the approach.