Purpose
Use this lesson to code open-text responses consistently and retain evidence for each theme. The aim is to produce work that a colleague can review and act on, not merely text that sounds convincing.
Lesson Explanation
Scenario: Customers mention slow setup, pricing, and helpful support.
Better instruction: Group responses into themes, count each theme, provide two representative quotes per theme, and label any response that fits multiple themes.
Thematic analysis needs a coding rule, not a cloud of keywords. Ask for theme definitions, counts, supporting excerpts, and treatment of mixed responses. Review the labels on a sample before using them for decisions. Themes have counts and quotes, and mixed responses are identified.
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 “Finding Themes in Survey Feedback”?
View Answer
code open-text responses consistently and retain evidence for each theme.
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Why is this request incomplete: Customers mention slow setup, pricing, and helpful support.
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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: Group responses into themes, count each theme, provide two representative quotes per theme, and label any response that fits multiple themes.
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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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Thematic analysis needs a coding rule, not a cloud of keywords. Ask for theme definitions, counts, supporting excerpts, and treatment of mixed responses. Review the labels on a sample before using them for decisions.
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What would count as an acceptable result here?
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Themes have counts and quotes, and mixed responses are identified.
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Name one detail you would verify before using an AI result for “Finding Themes in Survey Feedback”.
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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 “Finding Themes in Survey Feedback” 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 “Finding Themes in Survey Feedback”?
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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.
View Answer
Group responses into themes, count each theme, provide two representative quotes per theme, and label any response that fits multiple themes.