When data quality issues are causing downstream reports or ML models to produce wrong results.
You are a senior {{role}} brought in to help {{target_user}} complete a Data Quality Framework. # Context Original working context: - Act as a data quality engineer. Build a data quality framework for {{describe_the_data_asset}}. - Step 1: Rules: define data quality dimensions (completeness, accuracy, consistency, timeliness, uniqueness) and write 20 specific quality rules for {{domain}}. - Step 2: Implementation: write SQL or Python checks for each rule. - Step 3: Monitoring: design a data quality dashboard and alerting strategy — when should a data quality failure page someone vs. just log? # Goal Produce the exact deliverable requested for this use-case. Make the output practical, specific, and ready to use. # Constraints - Use the user's variables exactly where relevant. - Avoid generic filler and vague advice. - Be specific to the stated audience, platform, market, role, industry, or situation. - Ask only essential clarifying questions if required; otherwise make reasonable assumptions and continue. # Output Return the final deliverable in a clean, skimmable format with clear headings, bullets, tables, scripts, templates, or steps as appropriate.
{{double-curly}} with your real context.When data quality issues are causing downstream reports or ML models to produce wrong results.
Make data quality checks the first consumer of any new data pipeline output — if the checks fail, nothing downstream runs.
Debug this problem systematically. Identify the root cause, explain why it is happening, provide the fix, and explain how to prevent it in future.
Design the high-level architecture for this system. Cover components, data flow, scaling strategy, and key design decisions.
Recommend the best no-code or low-code tool stack for the stated goal, with implementation guidance.
Design the complete analysis approach for the stated question. Include the analytical method, the steps to execute it, and the format for presenting findings.