The article evaluates various language models (LLMs) to determine which one generates the most effective SQL queries. It compares the performance of these models based on their accuracy, efficiency, and ease of use in writing SQL code. The findings aim to guide users in selecting the best LLM for their SQL-related tasks.

Evaluating large language model (LLM) systems is complex due to their probabilistic nature, necessitating specialized evaluation techniques called 'evals.' These evals are crucial for establishing performance standards, ensuring consistent outputs, providing insights for improvement, and enabling regression testing throughout the development lifecycle. Pre-deployment evaluations focus on benchmarking and preventing performance regressions, highlighting the importance of creating robust ground truth datasets and selecting appropriate evaluation metrics tailored to specific use cases.