Role
Lead Product Research and Designer
Timeline
Dec 2024-Jun 2025
Team
Product Design
UX Researchers @ GitHub
Deliverables
Interactive Mocks
High Fidelity Prototype
Competitor Study
Design Guidelines
80%
Faster prototyping
5X
Faster MVP launches
↑ High
Higher user confidence
Scoping our audience, we found early- stage startup team and founders with little to no technical background are most motivated to build digital products independently using AI tool
🟣 Non-Technical Users: 0–1 yrs coding exposure, no dev background
🔵 Technical Users: 3+ yrs coding experience, active/prior SWE roles
Traditional Users
New Creators
Startup Developers
OSS
Maintainer
Professors / Academic Researchers
Non-tech
industry
professional
Enterprise Developers
Low Developer
Training/Experience
High Developer
Training/Experience
Small
Organization
Traditional Users
Large
Organization
Students
Startup Founders
Our Target Users
What problems do non technical founders face?
Non-technical founders face challenges in turning ideas into working products. Building functional prototypes often involves code dependency and steep learning curves, creating barriers to testing ideas and demonstrating concepts
Let’s see what our research uncovered
Our first round of interviews confirmed a growing demand for “vibe coding” tools: AI platforms that let non-technical users build intuitively through natural language and visual feedback.
Expanding GitHub’s creator base
AI enabling intuitive, code-free product workflows
Rising “build-by-feel” trend among early-stage Startups
I don’t trust it [The tool and output] because i don’t understand the code.
“
”
Customizing complex interactions for MVP or integrations quickly becomes difficult.
“
”
We compared non-technical users with developers using vibe coding tools and found a gap in control and clarity. While developers navigate with confidence, non-technical users need visibility and guidance. Our design closes this gap with more explainable AI interactions.
Use clear, simple language to build confidence
Show real-time visuals for transparent changes
Adapt AI tone and explanations to match each user’s technical comfort level
Let users test edits with clear feedback before committing changes
The Iteration Stage
Users build, test, and refine- is the main friction point in AI creation. Creators face unclear outputs and limited control, making iteration the biggest barrier to trust and progress.
Bridge to our Solution
Understanding the motivations and pain points of these early-stage, non-technical creators helped us define where Spark could truly make a difference — leading to two design principles that guided our solution and brainstorming
Personalized AI Interaction for non-technical users
Able to iterate without unwanted/unexpected changes, for better control and confidence
Creating concepts
Building Low Fi Wireframes
We brought our principles to life through quick sketches and flows, testing how guided feedback, visual previews, drag-and-drop editing, and simple code explanations could make AI feel clearer, more conversational, and easier to build with for non-technical creators.
After finalizing and designing our concepts, we conducted task-based think-aloud usability tests followed by retrospective interviews with non-technical startup founders, which led us to...
1
Switch to “Collaborate” mode so users can ask follow-ups in plain language
2
Enable plain language chat in Collaborate mode for in-the-moment guidance
3
Allow UI-to-code inspection by selecting elements to see highlighted code
1
Replaced toggle with tabs for clearer mode switching
2
Renamed “iterate” & “explain” modes to reduce confusion
3
Refined layout to separate Spark chat and theme controls
4
Added real-time code highlighting in the preview for clarity
5
Allow UI-to-code inspection by selecting elements to see highlighted code
6
Set technical level so Spark matches user’s language
Limitations
Our usability testing couldn’t capture quantitative measures of performance or large-scale validation, which limited how precisely we could evaluate impact. Time constraints also prevented us from testing live AI interactions, so some findings remained conceptual.
Learnings
The project taught us to creatively simulate AI complexity through prototyping and storytelling. We saw that user trust comes not from perfect automation but from clarity, transparency, and giving people confidence and control throughout the experience.
