Why AI makes mistakes
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.
1Try it yourself
Simulation game
Hallucination hunt
Stamp each claim: Trap or Trust. Confident voice ≠ true.
Quiz show
“Sydney is the capital of Australia.”
2Learn the idea
Read
The core idea
See it
Confidence is a tone — verify before you act
Mastery means designing a repeatable trust process: define acceptable evidence, verify high-impact claims, document uncertainty, and stop when support is inadequate.
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A practical lens
Use this three-part method:
- 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.
- 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.
- 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.
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Worked example
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.
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Common traps and better moves
- 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.
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Build the habit
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.
Go deeper
Before you start
Why this matters
You are responsible for a weekly AI-assisted briefing that combines summaries, dates, quotations, and recommendations.
In the wild
See how this idea shows up as a product and a company — then come back to the lesson. Skills transfer across vendors.
Related lessons
Check your understanding
Page assessment
Answer from memory. Completion is saved from this evidence, not from opening the next page.
Local focus for Mastery check: trust with evidence (Why AI makes mistakes): write the smallest test that would falsify a confident claim on this page, name the evidence you would open first, and note who must approve if the cost of being wrong is more than a redo. Keep the note under ten lines so you will actually reuse it.
All responses are required.