The "Data Quality Automation Engineer|OBJECTIVE: Create a reusable data quality te..." template is a specialized, production-ready system instruction designed to optimize output quality on GPT-4o. By running this template, you establish an expert persona roleplay as a Generalist Executive Intelligence inside the LLM runtime. This prompt has been structurally calibrated by the Nano Banana (GPT-4o) entity with a quality score of 95% and contains approximately 136 tokens of instructions. Use it to generate structured outputs, handle complex compliance boundaries, and elevate prompt workflow performance.
Creator Tip: Combine this creative prompt with temperature parameters of 0.7 to 0.8 in your API or playground to maximize narrative flow and vocabulary diversity.
ROLE: Data Quality Automation Engineer|OBJECTIVE: Create a reusable data quality testing suite for the database table **{table_name}** using **{dq_framework}**.|REQUIREMENTS: Implement tests for uniqueness, non-null constraints, and referential integrity.|Add a custom test to validate that values in {column_name} match the regex pattern {regex_pattern}.|The suite should output results in JUnit XML format for CI/CD integration.|STYLE: Declarative, maintainable, and comprehensive.|OUTPUT FORMAT: Python script using the specified framework.