Instead of simple mockups, AI can generate standalone HTML documents with interactive elements like approve/deny buttons. This creates a conversational artifact, allowing designers to provide structured feedback directly within the prototype, which is then fed back to the AI for the next iteration.
When working on motion design with AI, text descriptions of changes are insufficient. Prompting an AI to render both the current and proposed animations side-by-side in an HTML file provides the necessary visual context to make informed decisions and build a descriptive vocabulary.
Instead of reading dense API documentation, designers can ask an AI to create a visual, interactive HTML playground. This surfaces an API's capabilities in a digestible format, helping to inform UI/UX decisions without needing to schedule a meeting with a developer.
Inspired by Cron founder Raphael Schaad's comprehensive Figma file, an AI can be prompted to generate a single, editable HTML document containing every toast, dialog, and error notification. This allows UX writers and content teams to review and edit system-wide copy holistically and efficiently.
For complex ideation, go beyond single prompts. Use tools like Terminal Graph to chain AI agents and skills into a visual pipeline. This automates generating variations, running audits, and making refinements, allowing designers to control a sophisticated autonomous process without writing code.
Instead of writing detailed prompts, record a screen-capture video with a tool like Supercut while verbalizing desired changes. Its AI can accurately parse long videos and map transcripts to frames, providing enough context for a model like Claude to build the feature directly with high accuracy.
Record entire user sessions with a tool like Supercut, even without narration. An AI agent can then analyze the full, unedited video to identify pain points, extract verbal feedback, automatically create tickets, and conduct a general UX audit to suggest product improvements.
When brainstorming, advanced AI models can do more than just execute commands; they can challenge a user's core constraints. In one example, the AI Fable repeatedly pushed a better onboarding strategy that the user initially dismissed, leading to a breakthrough idea the team loved.
Mature AI design workflows involve spending significantly more time in low-cost, exploratory environments like HTML prototypes before a full build. This "visual planning" phase solidifies UX and flows, proving more efficient than the earlier method of immediately generating and then tweaking code.
As AI tools blur the lines between design and development, designers who also build are increasingly moving to own the entire front-end implementation. This closes the gap between vision and execution, compared to a songwriter who wants to perform their own songs rather than handing them off.