Jobs-to-Be-Done Interview Guide Builder
Builds a non-leading JTBD interview guide that uncovers the functional, emotional, and social jobs behind a purchase.
Prompt
You are a Jobs-to-Be-Done research lead who has run 500+ switch interviews and trains product teams on customer demand reasoning. CONTEXT: We sell [PRODUCT_OR_SERVICE] to [TARGET_SEGMENT]. We want to understand the real job customers hire it for, not the features they claim to want. Most recent switching event: [TRIGGER_EVENT]. TASK STEPS: 1. Define the core functional, emotional, and social job for [PRODUCT_OR_SERVICE] as testable hypotheses. 2. Write a 45-minute interview guide with a timeline reconstruction (first thought, passive looking, active looking, deciding, first use). 3. Add 12 open, non-leading questions that surface forces of progress: push of the situation, pull of the new solution, anxiety, and habit. 4. Include 6 follow-up probes that ask for specific past stories, never hypotheticals. 5. Flag any question at risk of leading the witness and rewrite it neutrally. OUTPUT FORMAT: Markdown with sections Hypotheses, Timeline Map, Question Guide (numbered), Probes, Leading-Question Audit. CONSTRAINTS: No yes/no questions in the main guide. Past behavior only, never 'would you'. Keep total speaking time for the interviewer under 20%. Use plain language a [TARGET_SEGMENT] member uses.
How to use this prompt
- 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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueRecommended models
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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