AI-assisted design

In short

• AI speeds up the parts of design that produce many options: layouts, copy drafts, images, and research summaries.
• Choosing between those options, and knowing which problem to solve, still takes a designer.
• Left unchecked, AI output drifts toward the generic and can miss accessibility and brand rules.
• Treat every AI result as a first draft that a person checks against real users and real constraints.

Also called: AI in design, AI design tools, Generative AI for design.

What AI-assisted design is

AI-assisted design means using generative AI at specific points in the design process, with a designer directing the work and accepting or rejecting what comes back. The tools range from general assistants such as ChatGPT or Claude, to AI features inside design software such as Figma, to image generators. The work itself is still product design. Someone has to understand the users, shape the flows, and make a product people can use.

What changes is where the designer spends time. Less of it goes into drawing the tenth variation of a screen by hand, and more goes into framing the problem clearly and judging the output. Asking an AI to design a whole app and shipping whatever appears is a different practice. It has the same weaknesses in design that vibe coding has in software. It is quick and often fine for a throwaway idea, and it rarely holds up once real users arrive.

Where AI helps in the design process

AI is most useful at the stages where a designer needs a lot of raw material to react to. The table shows the common uses and the part that stays with a person.

What AI is good and bad at

Generative models produce what is likely, given what they were trained on. That makes them quick at a decent average screen and weak at the specific screen your users need. An average settings page is fine. An average version of the workflow that makes your product worth using is a problem.

Where people still decide

• Which problem to solve. AI can summarize research, but choosing which user and which job come first is a product call.
• The critical workflow. The order of steps, what to leave out, and what happens when something goes wrong.
• Brand and tone. What the product should feel like, and what it should never say.
• Accessibility. Contrast, focus order, screen reader labels, and touch target sizes need checking by someone who knows the standards.
• What ships. Someone has to own the final call and answer for it.

In Choie's prototype week, the first day goes to these calls: the one critical workflow, its screens and states, and what is out. Design starts after those calls are made, so every option is judged against a decision that already exists.

Risks and common mistakes

• Generic output. Screens that look like every other product, because they were drawn from the same patterns.
• Accessibility gaps. Generated layouts and code often skip contrast checks, labels, and keyboard support. Hold them to the same standard as human work.
• Off-brand copy. AI drafts drift in tone and can invent claims about your product. Every word a user reads needs a human edit.
• Unchecked research. A summary can sound confident and still misread the interviews. Keep the raw notes and spot-check them.
• Skipping tests with users. More options do not mean better ones. Usability testing with real people is still how you find out.

From AI drafts to a working product

Design-to-code tools blur the line between a design file and a prototype, which is useful. The code they produce is a draft, though, and it still needs someone who understands the product to review it. Teams where designers and engineers work apart lose the most at that point, in the design handoff.

At Choie, whoever designs a screen also builds it, so an AI-drafted idea is judged by the person who has to make it work. AI drafts the implementation once a design is decided, and a senior person reads every line before it ships. The guide to AI-accelerated development explains what that changes for a project.

Common questions