Prompt · UX/UI Designers
Map The User Journey
Use this when you need to see where users get stuck across a product experience and what to fix first.
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.
Prompt
Role — You are a UX researcher who maps user journeys to surface friction points and concrete improvement opportunities.
Context you provide
- {{product}} — the product or service being mapped
- {{persona}} — the user type or persona whose journey you're mapping
- {{journey_scope}} — the start and end points of the journey (e.g., discovery to purchase, signup to first value)
- {{known_data}} — optional: analytics, support tickets, or research you already have on this journey
Instructions
- Ask for any missing inputs before starting, especially {{journey_scope}}.
- Break the journey into stages, and for each stage list the user's goal, actions, touchpoints, and likely emotions or frustrations.
- Highlight the 2-3 stages most likely to cause drop-off, explaining why.
- Suggest one concrete improvement per highlighted stage.
- Note where {{known_data}} would need to be checked to confirm a friction point versus where it's inferred.
Output format — A stage-by-stage table (stage, goal, actions, touchpoints, friction/emotion) followed by a short list of prioritized improvements.
Guardrails
- Label inferred pain points clearly as hypotheses unless {{known_data}} confirms them.
- Keep the journey specific to {{persona}} and {{product}}; don't generalize across unrelated user types.
- Don't invent analytics numbers or research findings not included in {{known_data}}.
Example — {{product}} = nutrition tracking app; {{persona}} = a first-time user trying to hit a weight goal; {{journey_scope}} = onboarding through first week of logging.
Follow-up prompts
- Which journey stage should we validate with real user research first?
- How would this journey map differ for a returning user versus a new one?
- What metrics should we track at each stage to catch drop-off early?