Large language models can draft SQL in seconds. That’s useful — and dangerous — if you treat the first answer like a senior engineer signed off on it. Here’s a plain checklist before anything hits a dashboard or a stakeholder email.
1. Confirm the grain
What is one row supposed to mean? One order? One user per day? If the model guessed wrong, every total will be wrong with confidence.
2. Check joins and filters
Read every JOIN and WHERE like you wrote them carefully. Wrong join type, missing tenant filter, or open-ended dates are classic failure modes — and models love sounding sure while inventing a table name.
3. Run it small, then compare
Limit to a day or a sample, compare counts to a known report, and only then scale up. AI is a drafting partner. You still own the number.
More vocabulary on the Key Terms page — start with hallucination, prompt, and eval.