AI for Design to Code: Keep Requirements Through the Handoff

AI can take a design and produce working HTML, a React component, or a styled block in minutes. The trouble is not the first draft. It is keeping the acceptance criteria, interaction states, and design decisions intact when the work moves from a designer to a developer to a code review. Tools like fenbs keep those decisions in a shared record outside any single chat, so the assistant picking up the next task starts from the same brief.

This guide covers what to document before you prompt an AI, what to check in its output, and how to handle the states that most first drafts miss.

Why design-to-code handoffs fail

A designer approves a layout. A developer asks an AI to build it. The AI produces something that looks right but handles loading, empty, and error states differently than the design intended. Neither person realizes until a user finds it.

The gap is almost never the visual output. It is the undocumented decisions: the font weight on mobile, what the empty state shows, whether an error message clears automatically. Those details live in the designer's head or in a comment thread the developer's assistant never read.

For a broader look at what AI can help with across a development workflow, the ai-at-work topic hub covers the territory.

Write acceptance criteria before you prompt the AI

Before asking an AI to write any interface code, write down what the component must do and when it must look different. A short list is enough: the default state, the loading state, the error state, the empty state, and any responsive breakpoints.

Give the AI that list in the first prompt. Do not rely on a description of the visual design alone. An AI that sees a screenshot and a style guide will produce code that looks correct, but it will guess at behavior it cannot see.

Keep the list with the task, not only in the chat. A brief that disappears with a session is not available to the reviewer or the next assistant who picks up a follow-on task.

Brief the AI on your codebase constraints

The AI needs to know the constraints it cannot read from the design: which component library you use, your CSS approach, any naming conventions, and the patterns already in the codebase. A brief that says "use Tailwind, functional components, no inline styles" produces more useful first drafts than one that says "build a card component."

Good AI code generators work best when the prompt includes an example of an existing component. Paste the patterns in, or link the relevant documentation. The more specific your brief, the fewer rounds of correction you need.

Check the output before you accept it

Check three things in the generated code before merging it. First, does every state from your list produce the right output? Test loading, error, and empty states with real or simulated data, not just a static render.

Second, does the code follow the conventions you specified? Mismatched conventions are hard to review and harder to maintain. Third, does it hold up in an AI code review against the design requirements? Ask the reviewer to check the component against the acceptance criteria, not only the code quality.

A step-by-step checklist for reviewing AI-generated code can help here. Run through it for the first review of any component before the work moves on.

When the designer and developer use different AI tools

A designer might use Claude for layout suggestions. A developer might use Cursor or Claude Code for implementation. Each tool starts from a blank context unless you give it the shared record.

The solution is a record outside both tools: the approved design decisions, the acceptance criteria, and the current task status. That is what each new session reads before acting. For an overview of what that record looks like, see what is an AI brain.

For building interfaces without writing any code, no-code AI tools covers that adjacent question.

Record decisions as you go

When the designer and developer agree on something, record it: the exact pixel value, the error message wording, the interaction on mobile. A decision that lives in one chat session is invisible to the next assistant.

Useful things to record: why a visual choice was made, what the accepted behavior is for each state, and what was reviewed and by whom. These are the facts a future assistant needs to extend the component correctly rather than guess at intent.

How to do this with fenbs

1. Create a free account and add a task for the component you are building.
2. Write the acceptance criteria in the task note: default, loading, error, and empty states, breakpoints, and any approved visual decisions.
3. Connect your AI tool over MCP from Integrations and ask it to read the task before writing code.
4. When a decision is approved, such as the error message wording, add it to Decisions and rules with the reason.
5. Before ending a session, update the task to say which states are reviewed and which remain. The next assistant reads this instead of asking.

A free personal board lets you test this alone. A team board lets a designer and developer both add and update tasks, with roles controlling who can approve a decision. For questions about following an agreed code of conduct with AI tools, see AI code of conduct.