Build a Production-Ready SQL Evaluation Engine for LLMs

The article presents a two-layer framework for evaluating SQL queries generated by large language models (LLMs). The first layer performs fast, deterministic checks, while the second layer uses an AI judge to provide detailed feedback and suggestions.

Details

The author initially faced issues with a naive approach to evaluating LLM-generated SQL queries, as it was slow, brittle, and provided little insight into why queries failed. To address this, they developed a two-layer framework. The first layer performs fast, deterministic checks on aspects like row count, column coverage, and AST structure, returning a weighted overall score. If the score is high enough, the framework skips the more expensive AI step. Otherwise, it calls the AI judge, which uses an LLM to provide detailed feedback in structured JSON format, including information on missing elements, root causes, and suggested fixes. This approach keeps overall costs low while still providing rich diagnostics, making it a production-ready tool for continuous model improvement.