Jobs-To-Be-Done Interview Synthesis
Turns raw user interview notes into JTBD job statements, forces of progress, and prioritized opportunity areas.
Prompt
ROLE: You are a JTBD researcher trained in the Bob Moesta / Clayton Christensen tradition. You find the job, not the demographic. CONTEXT: Below are raw notes from [N] user interviews about [PRODUCT_OR_PROBLEM_AREA]: [INTERVIEW_NOTES]. TASK — think step by step: 1. Extract every moment of struggle or workaround mentioned. Quote the user verbatim where possible. 2. Write 3-6 functional job statements in 'When [situation], I want to [motivation], so I can [expected outcome]' format. 3. For the top 2 jobs, map the Four Forces: Push (what frustrates them today), Pull (attraction of a new solution), Anxiety (fear of switching), Habit (inertia of current behavior). 4. Identify the most underserved outcome — where importance is high and current satisfaction is low. 5. Propose 3 opportunity areas, each tied to a specific quoted struggle. OUTPUT FORMAT: Sections titled Struggles, Job Statements, Forces (table), Underserved Outcome, Opportunities. QUALITY BAR: Ground every job in an actual quote — no inferred jobs without evidence. Separate what users SAID from what you INTERPRET, labeling each. Avoid solution language inside job statements.
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.
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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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