Explains setting a quality threshold from the use
A threshold is the error size that would change the report or decision the element feeds - the tolerance of the return or the payout - not an industry standard (BCBS 239 Principle 3; DMBOK2 ch. 13, 2.7.4).
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 | 91% | Not shown |
| gemini-3.5-flash-lite | Low cost | 53% | Not shown |
| gpt-6-luna | Low cost | 53% | Not shown |
| claude-haiku-5-5 | Low cost | 53% | 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: A threshold is the error size that would change the report or decision the element feeds - the tolerance of the return or the payout - not an industry standard (BCBS 239 Principle 3; DMBOK2 ch. 13, 2.7.4).
How it is assessed: Answers a written question on the quality threshold correctly
Related knobits
Needed for
All 10 knobits of Set data quality service levels and the scorecard.
Source: Practice the stage 7 reference and review checklist rest on