Mastery check: build a boundary map

Mastery means documenting useful lanes, prohibited lanes, evidence requirements, human owners, and signals that should stop or escalate the process.

Use this three-part method:

1. Write can, can-with-checks, and cannot-delegate columns. Write down what this means in the scenario, what evidence would show it was done, and who owns the decision.
2. Attach a reason and test to every boundary. Write down what this means in the scenario, what evidence would show it was done, and who owns the decision.
3. Review the map after incidents or capability changes. Write down what this means in the scenario, what evidence would show it was done, and who owns the decision.

Walk through You are deciding where an AI assistant belongs in a weekly workflow involving research, writing, personal data, and approval.. Label three moments where “Mastery check: build a boundary map” 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.

• Making permanent rules from temporary product limitations. This shortcut removes useful friction, but it also hides an assumption that should be tested. Replace it with an observable check.
• Loosening boundaries after a good demo. This shortcut removes useful friction, but it also hides an assumption that should be tested. Replace it with an observable check.
• Forgetting who remains accountable. 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: build a boundary map inside What AI can and can't do: 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 AI capabilities as task-specific and conditional rather than magical.