Resources
In-depth engineering guides: chunking, hybrid search, reranking, production RAG architecture.
Back to the blogRAG impresses in a demo and disappoints in production. Three patterns change everything: hybrid vector + BM25 search with RRF fusion, mandatory cross-encoder reranking, and decomposing complex questions into sub-queries.
Vector RAG retrieves isolated passages; GraphRAG links entities via a knowledge graph. Strengths, weaknesses, ingestion cost and a hybrid approach: when the graph genuinely changes the game.
Single-pass RAG fails on multi-hop and comparison questions. Agentic RAG lets an agent decompose, retrieve iteratively, and stop at the right time — with its guardrails and cost. When to use it, and when not.
Vector similarity grasps meaning but misses the literal (codes, acronyms). Hybrid dense + BM25 search fused with RRF, then cross-encoder reranking: the combo that makes a RAG genuinely precise.
The complete guide from RAG POC to production: chunking, embeddings, hybrid search + RRF, reranking, pgvector/HNSW, and RAGAS evaluation. 2026 technical choices, code, and a go-live checklist.
Your RAG's relevance is decided at chunking, before the embedding. 2026 strategies from fixed to semantic, late chunking and contextual retrieval, with trade-offs, pitfalls and concrete recommendations.