Overview
- RAG works by retrieving relevant document chunks, injecting them into the model context, and then generating answers, so retrieval quality now sets the ceiling for accuracy.
- Production failures usually trace to engineering problems such as poor chunking, missing metadata, and lack of observability rather than to the LLM itself.
- Best practices now include hierarchical chunking with source IDs and content hashes, hybrid dense plus sparse retrieval, and a two‑stage re‑rank (bi‑encoder recall then cross‑encoder) to balance recall and precision.
- Teams must instrument retrieval and generation with metrics like Recall@K, MRR, NDCG and faithfulness, plus signals such as I‑don't‑know rate and chunks‑dropped rate to detect silent degradation.
- Enterprises are adopting hybrid architectures that pair RAG for large or changing corpora with persistent KV caches that store attention state for hot documents to cut latency and cost while accepting context‑size and restore trade‑offs, and this combined approach is likely to dominate near‑term deployments.