This project involved engineering end-to-end data validation and quality assurance workflows for multi-million row structured datasets containing text-logic outputs from Large Language Models (LLMs). Using advanced SQL scripting, I built automated auditing parameters to map hidden performance drifts and identify structural output anomalies against strict compliance metrics. This workflow successfully isolated model errors, maximizing generative precision and data safety before deployment into live software pipelines.