This research explores the effectiveness of using Large Language Models (LLMs) as a judge to evaluate SQL generation, a key application of LLMs that has garnered significant interest. The study finds promising results with F1 scores between 0.70 and 0.76 using OpenAI's GPT-4 Turbo, but also identifies challenges, including false positives due to incorrect schema interpretation or assumptions about data. Including relevant schema information in the evaluation prompt can significantly reduce false positives, while finding the right amount and type of schema information is crucial for optimizing performance. The approach shows promise as a quick and effective tool for assessing AI-generated SQL queries, providing a more nuanced evaluation than simple data matching.