Strong at pattern work

Many current systems are useful at transforming patterns in supplied material: drafting, rewriting, summarizing, classifying, extracting, and generating alternatives.

Use this three-part method:

1. Supply relevant context and constraints. Write down what this means in the scenario, what evidence would show it was done, and who owns the decision.
2. Use outputs as candidates rather than unquestioned finals. Write down what this means in the scenario, what evidence would show it was done, and who owns the decision.
3. Choose tasks whose quality a person can inspect. Write down what this means in the scenario, what evidence would show it was done, and who owns the decision.

Walk through A communicator turns rough notes into three tones, groups survey comments by theme, and translates a draft for review.. Label three moments where “Strong at pattern work” 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.

• Asking for hidden facts instead of supplying source material. This shortcut removes useful friction, but it also hides an assumption that should be tested. Replace it with an observable check.
• Using a broad prompt with no success criteria. This shortcut removes useful friction, but it also hides an assumption that should be tested. Replace it with an observable check.
• Mistaking speed for completeness. 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 Strong at pattern work 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.