SQL/LLM Evaluation

This case study evaluates whether leading large language models can reliably support SQL-based business analytics workflows. As organizations increasingly experiment with AI-assisted data analysis, questions remain around model accuracy, consistency, privacy considerations, and cost effectiveness in real-world use. The project examined how GPT, Gemini, and Claude performed on structured SQL tasks using a controlled dataset and scoring framework, with the goal of assessing their practical viability for business decision-making environments.

The primary goal was to evaluate the accuracy and reliability of leading large language models when performing structured SQL-based analytics tasks. Secondary goals included comparing model performance across dimensions such as usability, consistency, privacy considerations, and cost, and determining whether premium models provided meaningful advantages over widely available alternatives in a business setting.

I structured the project as a controlled evaluation, defining a standardized set of SQL-based business questions using a common transactional dataset. Each model was prompted using consistent instructions, and outputs were assessed for query correctness, clarity of explanation, and completeness of results. I developed a scoring framework to compare performance across accuracy, usability, privacy considerations, and cost, then synthesized findings into a structured analysis highlighting strengths, limitations, and practical implications for business use.

A central decision was to evaluate models using standardized prompts and identical datasets to ensure comparability, prioritizing fairness and control over exploratory experimentation. This approach reduced variability but limited the ability to optimize prompts for each model's strengths. Another key tradeoff involved balancing evaluation depth with practicality, focusing on common business analytics tasks rather than edge-case technical complexity to better reflect real-world usage scenarios.

The analysis provided a structured, side-by-side comparison of leading large language models in the context of SQL-based business analytics. The findings clarified where models performed reliably, where inconsistencies emerged, and how cost and privacy considerations influence tool selection in professional environments. The final output delivered a practical evaluation framework that organizations can use to assess whether AI-assisted analytics tools meet their accuracy and governance standards.

This work strengthened my ability to evaluate emerging AI technologies while deepening my fluency in SQL-based business analytics. It sharpened my judgment around translating business questions into structured queries, validating data accuracy, and assessing model outputs for reliability and clarity. The project reinforced the importance of disciplined experimentation and analytical rigor when integrating AI tools into real-world data workflows.