Four weeks with Designlab’s course and the tools that stuck with me.
Why I Took the Course
When I signed up for a course on AI in UX design, I wanted more than theory. I wanted to see how AI could actually enhance my design practice, from research to delivery. Here’s what I learned, what I applied, and where I see the future heading.
As a mid-level product designer, I juggle research synthesis, wireframing, and prototyping. These tasks are valuable but often time consuming. I was curious - could AI help me move faster without losing depth?
Week 1: Framework Over Features
What I Expected: Tool demos and AI overviews.
What I Got: A strategic framework for AI integration and intimate mentor calls with small teams.
The breakthrough came early: learning to write structured prompts instead of random queries. Rather than “make this better,” I learned to craft: “As a UX designer creating an onboarding flow for a fintech app, suggest 3 layout approaches that reduce cognitive load while maintaining regulatory compliance.” And this was just the start!
Tools That Made the Cut:
• ChatGPT with prompt frameworks.
• Figma AI plugins for wireframes.
• Perplexity for competitive research.
• Midjourney for concept visualisation.
• Stitch for rapid wireframing.
Key Insight: AI integration requires the same strategic thinking as adopting any design tool, purpose before implementation. If this fails, you get a sub-par outcome. A good Acronym Chrissy used was - GIGO, Garbage In = Garbage Out. That stuck with me.
Week 2: Research Gets Faster, Not Smarter
The course’s peer group sessions reinforced this week’s focus: AI as intelligent synthesis partner, not replacement researcher.
Before AI Integration:
User interview analysis: 3-4 hours for 6 sessions.
Competitive research: Full day across multiple platforms.
Research-backed personas: 2+ hours of manual synthesis.
After AI Integration:
Interview analysis: 45 minutes with structured ChatGPT prompts
Competitive research: 2 hours with AI-generated comparison matrices.
Persona development: 30 minutes of synthesis + human validation
Here's a Real Example: During early user research for FitFuel (the courses project product), I fed interview transcripts into ChatGPT using structured analysis prompts. Instead of highlighting the obvious pain point of “slow workout plan setup,” the AI surfaced a deeper pattern: users weren’t frustrated with the time it took, but with not knowing what was happening in the background. That insight directly shaped our solution, introducing transparent progress indicators and contextual microcopy. By clarifying why steps were taking time, rather than just trying to speed them up, we built trust and reduced drop-off in the onboarding flow.
Week 3: Learning AI’s Limits
This week taught me the most important lesson: knowing when to trust AI and when to rely on human judgment.
Where AI Excelled:
Wireframe variations in minutes vs. hours.
Comprehensive usability test plans.
Consistent microcopy across components.
Where Human Expertise Won:
Understanding user emotions and context.
Making brand-aligned design decisions.
Knowing when to break conventions for better UX.
My Hybrid Workflow: AI generates options > Human judgment curates > AI accelerates production > Human oversight refines.
Result: Wireframe exploration dropped from 2 days to 4 hours, but I invested that saved time into more thorough user testing.
Week 4: Building Sustainable Habits
The final week focused on making AI integration stick beyond the course.
My Current AI-Enhanced Process:
1. Research: AI structures findings, I validate insights.
2. Ideation: AI generates variations, I combine and iterate.
3. Execution: AI speeds production, I focus on refinement.
4. Testing: AI organizes feedback, I interpret behavior.
Measurable Impact:
• Research synthesis: 70% faster.
• Design exploration: 60% more options in the same time.
• Strategic thinking time: 40% increase.
• Documentation consistency: Significantly improved.
Key Takeaways
After four weeks with Designlab’s AI course, I realised the real shift wasn’t about new tools, it was about judgment. Knowing when to lean on AI, and when to trust my own instincts.
Tools like ChatGPT (with custom prompts), Figma AI plugins, and Perplexity are now part of my daily workflow. They’ve cut research time by up to 70% and sped up design exploration by around 60%. But the real value wasn’t speed, it was skill building.
I learned:
Prompt engineering is now as essential as knowing Figma or core design principles.
Discernment matters - sometimes the best move is ignoring AI’s suggestions.
Blending outputs - using AI for breadth, then layering human insight for depth.
AI is excellent at generating options and clearing away the repetitive work. But the heart of design, understanding users, building empathy, and making strategic choices, remains human.
This course gave me a practical framework for using AI thoughtfully, not to replace design thinking, but to protect time for what matters most.. that is creating experiences that genuinely work for people.