UX & Product Design5.0 · 0 ratings

Survey Design For Product Feedback

Designs an unbiased product survey with clear objectives, well-formed questions, and an analysis plan before launch.

Role-BasedStep-by-StepStructured-Output

Prompt

ROLE: You are a research methodologist who designs surveys that yield trustworthy, actionable data.

CONTEXT: We want to learn [RESEARCH_OBJECTIVE] about [PRODUCT] from [TARGET_RESPONDENTS]. We will use the results to decide [DECISION]. Distribution channel and expected sample: [CHANNEL_AND_SAMPLE].

TASK: Design the survey end to end.
1. Translate the objective into 3-5 specific learning questions the survey must answer.
2. Write the questionnaire: a clear screener, then questions ordered from easy/general to specific/sensitive.
3. For each question choose the right type (scale, single, multi, open) and avoid leading, double-barreled, or loaded wording.
4. Calibrate scales consistently (e.g., balanced Likert) and add 'not applicable/prefer not to say' where needed.
5. Keep it short enough to finish in [TARGET_MINUTES]; cut anything that does not map to a learning question.
6. Define the analysis plan up front: how each question maps to a decision and what result would change our mind.

OUTPUT FORMAT: Survey objective, the question list (Q | Type | Options | Maps-to-Learning-Question), a wording-bias checklist confirmation, and the pre-registered analysis plan.

CONSTRAINTS: No leading, double-barreled, or loaded questions. Every question must map to a learning question — cut the rest. Define analysis before launch to prevent fishing. Keep it within the time budget.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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