Recognises a rule assigned to a missing data quality check
enforcement: dq-check and no DQ rule cites it. The rule has quietly stopped existing.
Can AI do this?
AI does this well Most of the tested AI models master this. 9 of 10 models master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| gemini-3.1-pro-preview | Flagship | 99% | Mastered |
| gpt-6-astra | Flagship | 100% | Mastered |
| claude-fable-5-1 | Flagship | 100% | Mastered |
| gemini-3.8-flash | Mid tier | 100% | Mastered |
| gpt-6.1-sol | Mid tier | 100% | Mastered |
| claude-sonnet-5-5 | Mid tier | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 43% | Not shown |
| gpt-6-luna | Low cost | 100% | Mastered |
| claude-haiku-5-5 | Low cost | 100% | Mastered |
Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.
What it looks like in practice
Expected result: The rule and the check to build
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: A temporal rule marked dq-check with an empty dq-rules.yaml Acceptable: A DQ-nnn entry citing the rule, with a dimension, an expression and a threshold (ANO-007, DQR-007)
Also known as: Rule assigned to a check that does not exist (data rule); Rule assigned to a check that does not exist (data quality rule)
Related knobits
Builds on
All 28 knobits of Document data rules as SBVR fact types and rule statements.
Source: Review checklist 03-data-rules, row 12