Engineering Team Cuts RAG Pipeline Latency 40% With Bayesian Search and Hybrid Retrieval

A development team overhauled their Retrieval-Augmented Generation (RAG) pipeline after standard production setups proved inadequate for legal contracts, API docs, 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, reducing the candidate pool from 50 to 5 results while gaining 15% recall improvement. A query transformation layer was also introduced to handle poorly phrased user queries before retrieval. The cumulative changes cut end-to-end query latency by 40%, bringing response times well below the one-second threshold common in naive RAG deployments.