Optimizing RAG at Scale: Chunking, Hybrid Retrieval, and Reranking for 95% Recall@10

Short summary

A detailed engineering guide to optimizing RAG pipelines at scale, covering four chunking strategies (fixed-token, recursive, semantic, agentic) mapped to document types, hybrid retrieval with reciprocal rank fusion, and cross-encoder reranking that improves recall by 15% for 50ms overhead. The post includes production config tables showing recall@10 metrics by document type and query transformation patterns. Claims a 40% latency reduction and 95% recall@10 through the combined approach.

• •Document-type-aware chunking: recursive for legal, semantic for tickets, agentic for wikis — each tuned for max recall
• •Hybrid retrieval with RRF fusion of vector + BM25, then cross-encoder rerank (50→5) gains 15% recall for 50ms
• •Query transformation layer expands and rewrites user queries before retrieval to handle poorly-formed questions

Generated with AI, which can make mistakes.