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-informed search approach further contributed to reducing overall query latency by 40%. The rebuilt pipeline demonstrates that tuning chunking and retrieval strategies to content type significantly outperforms one-size-fits-all RAG configurations at scale.