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 finding that standard fixed-token chunking and basic vector search failed 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. The hybrid approach, using Reciprocal Rank Fusion to merge results, added only 50ms of reranking overhead while delivering a 15% recall improvement. The overall pipeline redesign resulted in a 40% reduction in query latency and a measured retrieval recall of 95% at top-10 results.