Optimizing RAG at Scale: Document-Aware Chunking, Hybrid Retrieval, and Cross-Encoder Reranking

Short summary

A detailed engineering breakdown of rebuilding a RAG retrieval pipeline for production, moving beyond naive 512-token chunking to document-type-aware strategies (recursive, semantic, agentic) achieving 91-97% recall@10. The system combines hybrid vector+BM25 retrieval with reciprocal rank fusion and cross-encoder reranking, plus LLM-based query transformation to handle poorly-formed user questions.

• •Document-type-aware chunking: recursive for legal/API docs, semantic for tickets, agentic for wikis — each tuned for structure
• •Hybrid retrieval with RRF fuses vector and BM25 results, then cross-encoder rerank funnels 50→5 for 15% recall gain at 50ms cost
• •Query transformation uses LLM to expand poorly-formed user questions into multiple search queries before retrieval

Generated with AI, which can make mistakes.