A day in the life of an UX/UI Designer: what changes with these prompts.
Track progress as a memberNora, senior product designer at a 30-person health software company
On Tuesday morning, Nora has six interview notes, a booking flow that still feels clumsy, and a request to fix a low contrast button before review. She opens ChatGPT and uses the User Research prompt from the course. She pastes the messy quotes and asks for themes, pain points, and open questions. Twenty minutes later she has a one-page summary for her product manager.
Mid morning, she uses the Design Concept Development prompt to sketch three directions for the scheduling page. The constraints are simple: nurses book one-handed, and many patients use older phones. She asks Claude for trade-offs and picks the direction that keeps the first step short. She sets the order herself: date first, provider second, insurance last.
After lunch, she runs the Accessibility and Inclusive Design prompt before the Thursday review. It flags the gray cancel button and asks for a visible focus ring and clearer labels. She changes the color to a darker shade and writes the reason in the ticket. The small fix takes ten minutes, not a redesign.
Late afternoon, she uses the Prototyping and Wireframing prompt to turn the chosen layout into a clickable flow. The prompt gives her screen text, empty states, and notes for the developer. She adjusts two screens, then closes her laptop at 5:30. She walks to the train while it is still light.
Before
- Tabs open, notes everywhere, no clear next step.
- Contrast problems found late in review.
- Prototypes start from a blank page.
- Questions pile up faster than answers.
After this course
- One prompt turns messy notes into themes.
- Layout choices come with reasons attached.
- Accessibility checks happen before handoff.
- Prototype starts with a flow, not a blank page.
What you'll learn
- Clear problem framing: Turn fuzzy goals into precise questions that AI can address, with the right scope and constraints.
- Prompt strategy: Use context, roles, steps, and evaluation criteria so responses are relevant, consistent, and easy to compare.
- Evidence-led decision making: Tie AI outputs back to user needs, data, and accessibility standards.
- Structured artifacts: Produce research summaries, design rationales, layout specs, token lists, and test plans that are concise and reusable.
- Quality control: Review, stress-test, and iterate on AI suggestions using checklists that catch gaps early.
- Collaboration: Use AI to accelerate team alignment, handoffs, and documentation without losing craft.
- Ethics and safety: Reduce bias, protect privacy, and keep humans in the loop for critical decisions.
How this course works
- 15 lessonsOne task of your job each, from user research to analytics and user feedback.
- Ready-to-paste promptsCopy, fill in the parts in {{brackets}}, paste into ChatGPT, Claude or Gemini.
- Tick and completeTick the prompts you tried and mark each lesson complete.
- Get certifiedFinish with the exam and a certificate for LinkedIn.