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 512-token chunking and basic vector search proved inadequate in production environments. The team adopted document-specific chunking strategies — including recursive, semantic, and agentic methods — tailored to content types such as legal contracts, API docs, and support tickets. They replaced pure vector search with a hybrid retrieval system combining vector search, BM25, and cross-encoder reranking, achieving up to 97% recall@10 across document categories. A query transformation layer was also introduced to handle poorly formed user queries before retrieval. The combined optimizations resulted in a 40% reduction in query latency and significantly more reliable retrieval at scale.