Building a Production‑Ready SQL Evaluation Engine with Grok

Why You Need an Evaluation Engine for Text‑to‑SQL Every time I ask a language model to translate a natural‑language request into SQL, the first thing that comes back is a candidate query. If you’re building a product that powers analytics dashboards, billing reports or ad‑tech queries, a single wrong join can cost millions, and a missing filter could expose sensitive data. I spent months sifting through hundreds of generated queries to find subtle bugs—wrong aggregation, omitted columns, or even the dreaded Cartesian product. The solution? A two‑layer evaluation framework that combines fast deterministic checks with an AI judge that explains why something is wrong and how to fix it. Below I’ll walk you through the core ideas, show you the production‑ready code (no dashboards or storage needed), and explain how you can plug this into your existing workflow. TL;DR – Build a deterministic 80/20 checker + an LLM “judge” that returns JSON with missing elements, root causes, and a corrected