Build a Production‑Ready SQL Evaluation Engine for LLMs

Intro When I first started building a text‑to‑SQL system, the obvious thing was to run the generated query against a database and compare the result with a ground truth. That worked for a handful of examples, but as soon as we hit hundreds of user queries, the naive approach broke down: it was slow, brittle, and offered no insight into why a query failed. What I needed was a two‑layer engine: Fast deterministic checks that catch the most common mistakes in under a second. An AI judge that digs deeper when those checks fail, tells you exactly what’s missing or wrong, and even spits out a corrected SQL snippet. Below is my complete, production‑ready framework (no storage, no UI). I’ll walk through the architecture, show you the core code, and explain how to plug it into your own pipeline. By the end, you’ll have a reusable tool that turns every LLM‑generated query into actionable feedback—perfect for continuous model improvement. 1. Why Two Layers? Layer Purpose Typical Cost Speed Determ