Optimizing RAG at Scale: Chunking, Hybrid Retrieval, and Reranking That Cut Latency 40%

Short summary

A detailed walkthrough of rebuilding a RAG retrieval pipeline for production, covering four chunking strategies (fixed, recursive, semantic, agentic) tuned per document type with measured recall@10 metrics. The retrieval layer combines parallel vector and BM25 search via reciprocal rank fusion, then cross-encoder reranks top 50 to 5, gaining 15% recall for 50ms. Query transformation expands user questions before retrieval. The pipeline achieved 95% recall@10 and cut latency 40%.

• •Four chunking strategies (fixed, recursive, semantic, agentic) with per-document-type configs and measured recall@10
• •Hybrid retrieval: parallel vector + BM25, reciprocal rank fusion, cross-encoder rerank funnel (50→5)
• •Query transformation expands poorly-formed user questions before retrieval; pipeline hit 95% recall@10 and 40% latency reduction

Generated with AI, which can make mistakes.