AI is used to build out a signed-off design further with code and move it into local development workflows, making the handoff to developers faster and more concrete through working assets, design-system-aware refactors, and GitHub delivery.
The designer remains responsible for validating the design, deciding when it is ready, and ensuring the implementation matches user and business needs.
AI converts approved screens into starter frontend components so developers receive a concrete implementation baseline instead of static mockups alone.
v0 / Claude CodeAI creates routes, page structure, and sample states from the signed-off design to accelerate the move from prototype to repository.
Claude Code / GitHub CopilotAI helps generate implementation notes, code comments, and setup guidance in GitHub so the developer can understand the intended behavior faster.
GitHub Copilot / Claude CodeI help companies navigate AI adoption -- from strategy to production. Whether you are building your first LLM-powered feature or scaling an agentic system, I can help you get it right.
Design and build LLM-powered products and agentic systems
Go from idea to production with a clear implementation roadmap
Build AI with human-in-the-loop in regulated environments
CLI / end-to-end
And then there's the part I think designers should pay attention to: the workflow doesn't have to stop at the prototype.
OJO connects with Codex, Claude Code and Cursor, so the design can move into a local development workflow.
The interesting shift is:
Idea → product thinking → design → prototype → code
Instead of treating design and AI coding as completely separate workflows.
On my last product design project I went all-in with my AI workflow, and I wanted to share it here. Maybe it's useful to someone, and I'd love to get some tips to improve it.
I went from nothing to a fully working dashboard (6 pages, realistic data, all features functional) in days. Including UX research throughout the entire process. That's 5x faster than before.
Here's what my AI workflow looked like:
Discovery research
Stakeholder interviews and a user interview on the current tool. I record and transcribe every conversation. Then I use my own Claude skill to clean up those transcripts, summarize them and pull out to-dos. That output goes into 2 documents: 1 cleaned-up transcript, which I can still search through later. And 1 with a summary + to-dos.
Design exploration
I made 3 design directions in Figma (partly in Claude Design), discussed them with the team and stakeholders, and picked 1.
Prototype & testing
I then built a working prototype, still based on a lot of assumptions. Showed it to users. Again transcribed every conversation and ran it through my skill. That output goes straight into prompts for Claude Design.
Iterating
adjusted the design based on what I heard. Showed it again, transcribed again, prompted again. Research and design stop happening one after the other and start happening seamlessly alongside each other.
Live testing
I used Netlify to publish the prototype on a free link. Users shared their screen during a call, and I could watch and ask questions. It had fake data they could actually edit themselves, and immediately see the effect on the dashboard. It was simply a fully working dashboard.
Handoff to dev
once the design was fully signed off, I built it out further with Claude Code and handed it over to the developer via Github.
Result
5x faster than half a year ago. Not because AI takes over the work, but because the distance between insight and iteration gets a lot smaller. And I spend way less time on write-ups, re-reading, and typing.
Curious if this sparks any ideas to add (or drop) from this workflow!
And if you want to use my transcript skill, I can send it!