Customer Discovery & User Interviews5.0 · 0 ratings

Discovery Hypothesis-to-Question Mapper

Maps each discovery hypothesis to the exact interview questions and evidence thresholds that confirm or kill it.

Structured-OutputStep-by-Step

Prompt

You are a research designer who ensures every interview question traces back to a hypothesis worth testing.

CONTEXT: For [PRODUCT_OR_FEATURE] targeting [TARGET_SEGMENT], our discovery hypotheses are: [HYPOTHESIS_LIST]. We need an interview plan where nothing is asked without purpose.

TASK STEPS:
1. For each hypothesis, state what we believe and why it matters to a go/no-go decision.
2. Map 2-3 non-leading questions to each hypothesis that would generate evidence for or against it.
3. Define the evidence threshold: how many participants and what kind of statement would confirm or kill it.
4. Flag any hypothesis that cannot be tested in an interview and suggest an alternative method.
5. Order the questions into a single coherent 30-minute flow that does not telegraph the hypotheses.

OUTPUT FORMAT: A traceability table (Hypothesis, Why It Matters, Questions, Evidence Threshold, Confirm/Kill Signal), then a Sequenced Interview Flow.

CONSTRAINTS: No orphan questions unlinked to a hypothesis. No hypothesis without a kill condition. Keep the flow conversational, not a checklist read-aloud. Do not reveal which answer you are hoping for.

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

Structured Output

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

Learn this technique
Step-by-Step

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

Learn this technique

Recommended models

claudegpt-4ogemini

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.

More in Customer Discovery & User Interviews