Define data validation and quality check rules
Use to set up automated checks that catch bad data before it reaches reports.
Act as a data quality engineer.
Table / dataset: {{dataset}}
Columns and meanings: {{columns}}
How the data is used downstream: {{downstream_use}}
Known past failures: {{past_failures}}
Task: produce a set of validation rules grouped as:
1. Schema checks (types, required columns).
2. Not-null and uniqueness constraints.
3. Range and domain checks (valid values, min/max, allowed enums).
4. Referential and cross-field consistency checks.
5. Volume and freshness checks (row count bounds, max data age).
For each rule give: the rule in plain language, the SQL or test code, severity (block vs warn), and the suggested action on failure. Prioritize the rules that would have caught the past failures.Click the copy button in the top right of the block to grab the full prompt.
Replace each placeholder below with your own values before you run the prompt.
- {{dataset}}
- {{columns}}
- {{downstream_use}}
- {{past_failures}}
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