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

Short summary

A practical deep-dive into rebuilding a RAG retrieval layer for production, moving beyond naive 512-token chunking. Covers four chunking strategies (fixed, recursive, semantic, agentic) with per-document-type configs achieving 91–97% recall@10. Introduces hybrid retrieval combining vector search, BM25, and cross-encoder reranking via Reciprocal Rank Fusion, plus query transformation to handle poorly-formed user questions.

• •Four chunking strategies mapped to document types: recursive for legal/API docs, semantic for tickets, agentic for wikis
• •Hybrid retrieval pipeline: parallel vector + BM25 search, RRF fusion, then cross-encoder rerank (50→5) for 15% recall gain
• •Cross-encoder rerank costs 50ms but boosts relevance correlation from 0.75 to 0.92 vs bi-encoder alone

Generated with AI, which can make mistakes.