Recognises a metadata freshness the capture method cannot meet
A required freshness faster than the method can deliver.
Can AI do this?
People still lead Few or none of the tested AI models showed they master this. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 72% | Not shown |
| gemini-3.5-flash-lite | Low cost | 42% | Not shown |
| gpt-6-luna | Low cost | 47% | Not shown |
| claude-haiku-5-5 | Low cost | 69% | Not shown |
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 gap and a capture method that meets the freshness
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: UC-001 needs lineage "same day as a pipeline change"; MX-002 is a quarterly manual wiki update while pipelines deploy daily Acceptable: Lineage harvested by SQL parsing on each deployment
Also known as: Freshness the capture method cannot meet
Related knobits
Builds on
All 13 knobits of State the metadata requirements and strategy.
Source: Review checklist 01-requirements-strategy, row 5 (DMBOK §2.1)