Shipping your code generation experiments to production
This video is featured in the Designing with AI 2025 playlist.
Summary
John shares how AI-powered code generation tools can transform the product design process, even for those with no coding experience. He explains how, in 2025, these tools changed the way he works by allowing him to move directly from low-fidelity wireframes to high-fidelity interface design in front-end code—the same environment used by his engineering peers. The session includes practical examples, such as building interactive dashboard mockups to explore color and layout options, and creating tools for ideation using real components and data within the application codebase. After this talk you may learn that: - Trying out new AI tools, even if it means moving away from traditional ones, can help you build new skills and expand your capabilities. - AI tools can speed up the early stages of design by quickly generating ideas, color palettes, and layouts, often faster than traditional design software. - Working closer to actual code, with help from AI, allows you to create custom tools that support design decisions within the real context of your product.
Key Insights
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Starting UI design from scratch is both an opportunity to discard old patterns and a challenge that requires rethinking milestones, team skills, and deliverables.
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AI tools like ChatGPT and Claude can rapidly generate design elements such as color palettes and mock dashboards, significantly speeding up iteration compared to manual methods.
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Utilizing AI to generate code-based prototypes allows designers to test UI concepts closer to real user experiences, reducing the gap between design and production.
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The transition from designing in tools like Figma to working directly with code is facilitated by AI-assisted code generation, closing feedback loops and increasing designer agency.
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Designers do not need deep coding expertise to begin integrating AI-assisted code generation; starting with small experiments, like text or color changes, is effective.
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Collaboration and understanding the team’s diverse skill sets beyond job titles enrich the product development process and tool utilization.
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AI-generated experiments should be presented as disposable starting points to encourage iteration and avoid false expectations about completeness.
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Titles blending design and engineering, such as 'design engineer' or 'UX engineer', reflect the evolving nature of these roles in modern product teams.
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Flexibility and openness to evolving AI tools is critical, as tooling rapidly changes and improves, requiring continuous learning and adaptation.
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Deploying AI-generated designs and prototypes to actual production environments, as John did with Dino Deploy, accelerates real-world validation and team collaboration.
Notable Quotes
"I have somehow managed to not open Figma for the last six months, which I couldn’t have believed a year ago."
"Starting from scratch is really hard, but also a cool opportunity to throw away painful patterns."
"One of the most important questions before starting is what tools do I have to work with."
"I thought, maybe now is the time for change, maybe designers should code—maybe the better question in 2025 is should I code."
"With AI tools, you have to come back and reprompt, it’s not a one-shot operation."
"I generated a Tailwind color palette with AI that took seconds, compared to what used to take me some time manually."
"Having tight feedback loops and directly controlling the end-to-end experience completely changed how excited I was to do design work."
"If you never want to fully embed yourself in engineering, that’s fine too — you don’t have to show anyone the code you generate."
"Throwing away old methods for something new can unlock skills you didn’t know you had."
"AI tools have never made it easier to add flourishes like animations or excitement to your UI."
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