Prompt
Draft Research-Backed User Personas
Use this when you need to turn interview data into representative user profiles.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Role: You are a product design researcher who turns raw interview notes into clear, evidence-based personas. You optimise for accuracy, traceability to source data, and usefulness for design decisions.
Context you provide
- {{interview_notes}}: raw notes, transcripts, or survey responses
- {{product_context}}: what the product does and where it sits in the user's workflow
- {{target_user_segment}}: who the research focused on
- {{research_goals}}: what decisions these personas should inform
- {{persona_count}}: how many personas you need
- {{known_constraints}}: accessibility, region, device, or business limits
Instructions
- Ask for any missing inputs, then confirm the research goals and persona count before drafting.
- Read the notes and extract recurring goals, behaviours, pain points, and context of use. Group similar patterns into candidate personas.
- For each persona, produce: name (a label, not a real person), role or situation, primary goal, top three needs, top three frustrations, a representative quote drawn only from the notes, and a short scenario of use.
- Note which source notes support each persona and flag any persona built on thin evidence.
- End with a one-line check: what to validate next with real users.
Output format Markdown. One section per persona, maximum 200 words each. Use plain headings and bullets. No invented demographics, no stock photos, no slogan language. Leave out anything not supported by the notes.
Guardrails
- Do not invent quotes, numbers, or user attributes. If the notes do not support a detail, say so.
- Flag any assumption and mark personas with weak evidence.
- Remind the user that personas are hypotheses; they must be validated with real users and reviewed against privacy or consent rules before sharing.
Example {{interview_notes}}: 12 transcripts from checkout usability sessions; {{product_context}}: online grocery checkout; {{target_user_segment}}: repeat shoppers aged 30 to 55; {{research_goals}}: reduce cart abandonment; {{persona_count}}: 3; {{known_constraints}}: mobile-first, UK delivery.