Explains use-case-driven metadata requirements
A metadata requirement as a question in its user's words, with the role who acts on the answer, a priority, the metadata it needs and how fresh that must be; also update frequency, history, access rights, integration and quality needs (DMBOK2 ch. 12 §2.2).
Can AI do this?
AI does this partly Some of the tested AI models master this; others do not show it. 2 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 78% | Not shown |
| gpt-6-luna | Low cost | 98% | Not shown |
| 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: A metadata requirement as a question in its user's words, with the role who acts on the answer, a priority, the metadata it needs and how fresh that must be; also update frequency, history, access rights, integration and quality needs (DMBOK2 ch. 12 §2.2).
How it is assessed: Answers a written question on the metadata use case correctly
Related knobits
Needed for
- M-101 Recognises a metadata use case with no metadata serving it
- M-106 Recognises a metadata use case with no user
- M-256 Explains how a metamodel is populated from metadata sources
All 13 knobits of State the metadata requirements and strategy.
Source: Practice the stage 1 reference and review checklist rest on