Use case 01
When creating a platform-wide data quality program.
Data EngineerData QualityAdvancedChain
Data Quality Framework Chain AI Prompt
This prompt assembles a full data quality operating model, not just individual checks. It covers critical-table selection, testing, severity, routing, scorecards, incidents, and continuous feedback. It is best used when an organization wants a formal DQ framework with ownership and response expectations.
Prompt text
Step 1: DQ requirements — identify the top 10 most critical tables in the platform. For each, define: the business impact of bad data, the acceptable error rate, and the SLA for detecting and resolving issues. Step 2: Test implementation — for each critical table, implement the full test suite: schema, freshness, row count reconciliation, business rule validation, and statistical anomaly detection. Step 3: Severity and routing — classify each test by severity (blocking vs warning) and define the alert routing: who gets notified, by which channel, and within what time window. Step 4: DQ scorecard — build a daily DQ scorecard: overall pass rate, test pass rate per table, trend over time, and highlight tables with declining quality. Step 5: Incident workflow — define the incident workflow for DQ failures: detection → acknowledgment → investigation → root cause → fix → post-mortem. Define SLAs per severity. Step 6: Feedback loop — create a mechanism for analysts to report suspected data quality issues. Triage reported issues, trace to root cause, and update tests to prevent recurrence. Step 7: Write the DQ framework document: test inventory, severity matrix, alert routing, SLAs, incident workflow, and the governance process for adding new tests.
When to use this prompt
Use case 02
When critical tables need differentiated SLAs and alerting.
Use case 03
When incidents must be managed consistently across teams.
Use case 04
When you want a DQ framework document, not only test code.
What the AI should return
Return a full framework covering table prioritization, test inventory, severity matrix, routing rules, scorecard design, incident workflow, and governance. Include how issues are reported, triaged, fixed, and prevented from recurring. The result should read like a data quality operating model for the organization.
How to use this prompt
1
Open your data context
Load your dataset, notebook, or working environment so the AI can operate on the actual project context.
2
Copy the prompt text
Use the copy button above and paste the prompt into the AI assistant or prompt input area.
3
Review the output critically
Check whether the result matches your data, assumptions, and desired format before moving on.
4
Chain into the next prompt
Once you have the first result, continue deeper with related prompts in Data Quality.
Frequently asked questions
What does the Data Quality Framework Chain prompt do?+
It gives you a structured data quality starting point for data engineer work and helps you move faster without starting from a blank page.
Who is this prompt for?+
It is designed for data engineer workflows and marked as advanced, so it works well as a guided starting point for that level of experience.
What type of prompt is this?+
Data Quality Framework Chain is a chain. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.
Can I use this outside MLJAR Studio?+
Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.
What should I open next?+
Natural next steps from here are Data Lineage Tracking, Data Quality Test Suite, Duplicate Detection at Scale.