Purpose
Use this lesson to frame an analysis around a business decision, metric, comparison, and time period. The aim is to produce work that a colleague can review and act on, not merely text that sounds convincing.
Lesson Explanation
Scenario: Sales fell last month and leadership wants an explanation.
Better instruction: Analyze month-over-month sales by product and region; identify the largest absolute and percentage changes; separate observed patterns from possible explanations.
“Analyze this data” is too broad. Identify the decision, metric definition, comparison period, segmentation, and distinction between facts and hypotheses. This keeps the output from presenting correlation as certainty. The analysis specifies products, regions, month-over-month comparison, and separates observations from explanations.
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 “Asking Better Analysis Questions”?
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frame an analysis around a business decision, metric, comparison, and time period.
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Why is this request incomplete: Sales fell last month and leadership wants an explanation.
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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: Analyze month-over-month sales by product and region; identify the largest absolute and percentage changes; separate observed patterns from possible explanations.
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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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“Analyze this data” is too broad. Identify the decision, metric definition, comparison period, segmentation, and distinction between facts and hypotheses. This keeps the output from presenting correlation as certainty.
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What would count as an acceptable result here?
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The analysis specifies products, regions, month-over-month comparison, and separates observations from explanations.
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Name one detail you would verify before using an AI result for “Asking Better Analysis 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 “Asking Better Analysis 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 “Asking Better Analysis 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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Analyze month-over-month sales by product and region; identify the largest absolute and percentage changes; separate observed patterns from possible explanations.