Recognises a model with no repository or baseline
Nowhere the versions are kept and tagged.
Can AI do this?
AI does this well Most of the tested AI models master this. 3 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 91% | Mastered |
| gemini-3.5-flash-lite | Low cost | 10% | Not shown |
| gpt-6-luna | Low cost | 91% | Mastered |
| claude-haiku-5-5 | Low cost | 91% | 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 repository and the tagging of baselines
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: tools: {repository: null} Acceptable: repository: "git: reffa/models", tags: model-<version>
Also known as: No repository or baseline
Related knobits
Builds on
- K-916 Explains data model versioning and change management
- K-917 Explains the tools supporting data modelling
Usually followed by
All 10 knobits of Plan the modelling and set its standards.
Source: Review checklist 00-planning-standards, row 6 (DMBOK §6 (versioning))