AI coding assistants can knock out a SQL query in seconds. You describe what you want in plain English, and out comes a SELECT statement that looks completely reasonable. So you copy it, paste it into your app or dashboard query runner, and hit execute.

This is where things go wrong.

Unlike a syntax error that crashes loudly, a semantically wrong SQL query runs successfully and returns a result. The result looks plausible. You ship the dashboard. A week later, someone notices the revenue numbers are 15% off because the AI joined on the wrong foreign key. The silent failure is the dangerous one.

This post is a practical checklist for validating AI-generated SQL before it touches your production database — whether you're building a reporting feature, an embedded dashboard, or using a text-to-SQL tool internally.

Why AI Gets SQL Wrong

Large language models generate SQL by predicting likely token sequences based on training data.…