How I use GenAI in my UX workflow without losing the craft

I have been designing digital products since 2007. In that time the tools changed many times, from Photoshop slices to Sketch, InVision, Zeplin and Figma. Generative AI is the biggest shift I have seen, because for the first time the tool can produce a reasonable first draft of the work itself.

That is exciting and a little dangerous. Here is how I use it, and where I draw the line.

Where AI drives

Exploring many options quickly. Early in a project I want breadth. Tools like ChatGPT help me list user goals, edge cases and alternative flows I might not have considered. Uizard and Motiff help me sketch screen ideas in minutes instead of hours.

Turning flows into prototypes. Once a flow is agreed, tools such as Bolt.new and LLM coding copilots help me turn it into a clickable prototype or front-end code fast. For CorpSakha, my own product, this is how I went from a written brief to a prototype covering 96 screens, from onboarding and verification to chat and an admin console, running as an installable Android app for testers.

The boring but necessary parts. Placeholder content, sample data, first drafts of microcopy, repetitive component variants, documentation outlines. AI is very good at volume.

Where I never let it drive

Framing the problem. The brief, the audience, the constraints and the one-line positioning are mine. If those are wrong, AI simply produces the wrong thing faster.

Judgement about people. AI does not sit in a stakeholder interview. It does not see a CEO frown at a chart or a nurse struggle with a form. Research and empathy remain human work.

Trust and safety decisions. In a product like CorpSakha, decisions such as requiring mutual consent before chat, or making report and block one tap away, are product ethics. They need to be designed deliberately, not generated.

Accessibility. I check contrast, focus order, labels and states by hand. Generated UI often looks fine and fails a keyboard user in seconds.

My four-step loop

Frame. Write the brief and success criteria myself.
Generate. Use AI tools to explore flows, screens and code in parallel.
Curate. Pick, merge and rewrite. Every screen gets a designer's review for copy, empty and error states, edge cases and accessibility.
Ship. Turn the result into a prototype people can test, or a production front end.

AI is fast. It is not the strategist, and it is not accountable. I am.

Prompting is a design skill

Good prompts look a lot like good design briefs: context, audience, constraints, examples of what good looks like, and what to avoid. The skills I built writing user stories and acceptance criteria over many enterprise programmes turned out to be exactly what makes AI output useful.

A few habits that help:

Give the model the design system: tokens, components, naming. Otherwise it invents its own.
Ask for states explicitly: loading, empty, error, success, permission denied.
Ask for several variants, then combine the best parts yourself.
Keep a human-readable spec next to generated code, so the team knows what was intended.

What changes for clients

The practical result is speed without skipping steps. Clients see a working prototype much earlier, which means better feedback earlier. The time saved on production goes back into research, review and polish, which is where design quality actually comes from.

GenAI did not replace my craft. It moved it. Less time pushing pixels, more time deciding which pixels deserve to exist.