Engineering Team Cuts RAG Pipeline Latency 40% With Hybrid Search and Smarter Chunking

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 identified that fixed-token chunking caused problems across document types, splitting legal clauses mid-sentence and burying signal in oversized API documentation chunks. They replaced the baseline approach with document-type-specific strategies — including recursive, semantic, and LLM-guided chunking — achieving recall@10 scores between 91% and 97% depending on content type. On the retrieval side, they combined vector search, BM25 keyword search, and a cross-encoder reranker using Reciprocal Rank Fusion, with the reranking step alone improving recall by 15%. The full pipeline overhaul resulted in a 40% reduction in query latency and a measured 95% recall@10 across their production workload.