Retrieval-augmented generation is the default pattern for grounding large language models in private data. The demo is easy; the production system is not. The gap is mostly about retrieval quality, evaluation, and guardrails.
Start with the data. The single biggest lever on answer quality is how you chunk and index documents. Semantic chunking that respects document structure consistently beats naive fixed-size splitting.
Next, evaluate ruthlessly. Build a test set of real questions with known answers and measure retrieval precision and answer accuracy on every change. Without evaluation you are tuning blind.
Finally, add guardrails: citations so users can verify, refusal behavior when context is missing, and cost controls so a single query can't run away. Done right, RAG becomes a system you can trust.
