Mastery check: trust with evidence

Mastery means designing a repeatable trust process: define acceptable evidence, verify high-impact claims, document uncertainty, and stop when support is inadequate.

Use this three-part method:

1. Write a claim-checking standard. Write down what this means in the scenario, what evidence would show it was done, and who owns the decision.
2. Assign sources and reviewers to important claim types. Write down what this means in the scenario, what evidence would show it was done, and who owns the decision.
3. Measure errors and improve the workflow over time. Write down what this means in the scenario, what evidence would show it was done, and who owns the decision.

Walk through You are responsible for a weekly AI-assisted briefing that combines summaries, dates, quotations, and recommendations.. Label three moments where “Mastery check: trust with evidence” changes what you trust: (1) the first fluent answer, (2) the first missing source or permission, and (3) the decision a human must own. Write the before/after task so the model only does the slice that evidence supports. Keep one sentence that states how this page’s idea differs from a generic “AI is smart/dumb” score.

• Relying on memory after the briefing ships. This shortcut removes useful friction, but it also hides an assumption that should be tested. Replace it with an observable check.
• Tracking only obvious errors reported by others. This shortcut removes useful friction, but it also hides an assumption that should be tested. Replace it with an observable check.
• Calling a workflow safe because it worked last week. This shortcut removes useful friction, but it also hides an assumption that should be tested. Replace it with an observable check.

Before you close the tab, capture a reusable habit for Mastery check: trust with evidence inside Why AI makes mistakes: name the observable check, the evidence you would open, and the stop condition. Rehearse it once on a low-stakes example, then once on a higher-stakes variant. The habit succeeds when you can explain the check without reopening this lesson. Target outcome: Explain why plausible language generation does not guarantee factual accuracy.