Vibe Coding at Google: Prototyping the all-new AI Studio
A tutorial by Dive Club. Featured in the OTF curated resource library.
The Context: Redesigning AI Studio
Google AI Studio is the primary interface for developers working with Gemini models. When Google decided to redesign the experience, they faced a classic product challenge: how do you explore dozens of design directions quickly enough to make informed decisions?
The twist: they used their own AI (Gemini) to prototype the new interface. It's a delightful case of AI eating its own dogfood — using AI tools to build better AI tools.
The design team described their desired interfaces in natural language, Gemini generated working HTML/CSS/JS prototypes, and they iterated rapidly through design explorations. What would normally take weeks of designer-developer collaboration happened in days.
The Gemini Prototyping Workflow
The Google team developed a specific workflow for AI-assisted prototyping.
Start with design intent, not specs
Instead of detailed wireframes, the team described the experience they wanted: 'A clean, focused interface where developers can quickly test prompts, see results in real-time, and switch between models without losing context.' This high-level intent guided the AI better than pixel-level specifications.
Generate multiple directions simultaneously
Ask Gemini for 3-4 different layout approaches for the same intent. A sidebar-based layout, a top-bar layout, a split-pane layout, and a minimal single-column layout — all generated in minutes. Compare real implementations, not wireframes.
Iterate on the most promising direction
Pick the best direction and refine: 'Make the model selector more prominent, add a history panel on the right, and increase the contrast between the input and output areas.' Each iteration takes minutes, not hours.
Hand off the validated prototype to engineering
The AI-generated prototype isn't the production code — it's a validated direction. Engineering takes the prototype as a reference and builds the production version with proper architecture, testing, and performance optimization.
What Worked
Speed of Exploration
The team explored 20+ design directions in the time it would normally take to fully design 2-3. This breadth of exploration led to design decisions they wouldn't have discovered through traditional methods.
Stakeholder Alignment
Showing stakeholders working prototypes (not wireframes) dramatically reduced miscommunication. Executives could click through the interface and give feedback on real interactions, not static mockups.
Design-Engineering Communication
When designers hand off a working prototype, engineers understand the intent at a much deeper level. The prototype answers hundreds of implicit questions that static designs leave ambiguous.
Rapid User Testing
Working prototypes enabled real user testing within days of a design concept. Users interacted with functional interfaces, providing higher-fidelity feedback than wireframe walkthroughs.
Challenges and Limitations
Code Quality Wasn't Production-Ready
AI-generated prototypes were functional but not architecturally sound. They served as design validation, not production code. Engineering rebuilt from scratch using the prototypes as visual reference.
Complex Interactions Were Hard to Describe
Simple layouts generated well, but complex interactive patterns (drag-and-drop, multi-step wizards, real-time collaboration) required more iterative prompting and manual refinement.
Design System Consistency
Each AI-generated prototype had slightly different styling. The team had to explicitly reference Google's Material Design tokens in every prompt to maintain visual consistency across explorations.
Takeaways for Product Teams
Google's experience offers transferable lessons for any product team:
Separate exploration from production. Use AI for rapid design exploration, then have engineers build the production version. Trying to make AI prototypes production-ready adds complexity without proportional benefit.
Invest in design tokens early. A well-defined design system (colors, fonts, spacing) that you reference in every prompt dramatically improves consistency across AI-generated prototypes.
Show, don't describe. Working prototypes are more persuasive than any document. Use AI-generated prototypes for stakeholder reviews, user testing, and engineering handoffs.
The prototype is disposable — the decisions aren't. The value of AI prototyping isn't the code. It's the design decisions you make 10x faster because you can explore more broadly and validate more quickly.
Meta-lesson: Use your own tools. Google using Gemini to build Gemini's interface is the ultimate feedback loop. If your company builds AI tools, use them in your own design process.