Purpose
Use this lesson to make missing inputs visible before drafting. The aim is to produce work that a colleague can review and act on, not merely text that sounds convincing.
Lesson Explanation
Scenario: Create a sales proposal for a new prospect with almost no details.
Better instruction: Before drafting, ask up to five questions about the customer problem, scope, budget range, timeline, and decision maker.
The fastest route is sometimes a question, not an answer. Tell the model to pause when a missing fact would materially change cost, commitment, audience, or advice. Limit the questions so discovery does not become a questionnaire. The questions address problem, scope, budget, timeline, and decision maker.
Work in a short cycle: provide the relevant input, state the result and limits, inspect the draft against the acceptance criteria, then revise the instruction when a requirement is missing. Keep the final human decision with the person responsible for the work.
Practice Questions
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What is the main work outcome in “When the AI Should Ask Questions”?
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make missing inputs visible before drafting.
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Why is this request incomplete: Create a sales proposal for a new prospect with almost no details.
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It leaves important decisions to guesswork. The lesson shows how to supply the missing purpose, context, boundary, or format.
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Which instruction makes the request operational: Before drafting, ask up to five questions about the customer problem, scope, budget range, timeline, and decision maker.
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It defines a concrete result that can be checked instead of asking for a vague response.
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What principle should guide your prompt for this lesson?
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The fastest route is sometimes a question, not an answer. Tell the model to pause when a missing fact would materially change cost, commitment, audience, or advice. Limit the questions so discovery does not become a questionnaire.
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What would count as an acceptable result here?
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The questions address problem, scope, budget, timeline, and decision maker.
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Name one detail you would verify before using an AI result for “When the AI Should Ask Questions”.
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Verify the source facts, inputs, numbers, names, dates, policy limits, or assumptions that affect the real decision.
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What should you add if the result is polished but not usable for the scenario in this lesson?
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Add the missing acceptance criteria or output structure, then regenerate and compare against the stated requirement.
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Which is safer: asking for a general answer or stating the business context in this lesson? Why?
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State the business context because it reduces irrelevant guesses and lets the AI tailor the work to the actual situation.
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How would you test the output from “When the AI Should Ask Questions” before sharing it?
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Check it against the requested facts, format, limits, and acceptance criteria; then have the appropriate human reviewer approve it.
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What should the AI do when a required fact is missing in this scenario?
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Flag the missing fact or ask a focused question rather than silently inventing an answer.
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What risk does the lesson warn about for “When the AI Should Ask Questions”?
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Treating fluent output as automatically correct, complete, approved, or fit for real-world use.
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Write the shortest useful improvement to the weak request in this lesson.
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Before drafting, ask up to five questions about the customer problem, scope, budget range, timeline, and decision maker.