RAG combines retrieval with generation to ground LLM responses in your data. Most RAG demos work; most production RAG systems don't. Here's why.
The checklist
Data quality
- [ ] Clean, chunked documents with metadata
- [ ] Deduplication and freshness strategy
- [ ] Evaluation dataset with ground truth
Retrieval
- [ ] Hybrid search (semantic + keyword)
- [ ] Reranking for precision
- [ ] Query transformation/expansion
Generation
- [ ] Citation of source documents
- [ ] Fallback when retrieval confidence is low
- [ ] Response validation
Operations
- [ ] Latency and cost monitoring
- [ ] User feedback loop
- [ ] Regular eval runs on production traffic samples
The hard truth
RAG is an engineering problem, not a library problem. Invest in evaluation early.