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As of 13 August 2026, AI can only partly turn your UX design into React code.
This still needs a person who signs their name to it.
Can you do it?
15 minutesto a draft.
1 hourto something you’d act on.
Cost, all in£0
Skill neededpower-user
Who has to check ita colleague
What the alternative costsThe supplied data does not give a price for a developer-built React implementation.
If this goes wrong, you get a convincing interface with broken states, inaccessible controls or integration defects that take further development work to find and fix.
What to actually do
Hand it to a person
The route this page recommends
A person who owns the outcome does this end to end, worth it when the failure is dear.
Use a tool built for this
Second choiceDo it yourself
The distant thirdA chat interface, power-user skill, and roughly 1 hour until you can act on the result.
How to actually do it
- Open the design in Figma or your design tool and export screenshots for each screen, including desktop and mobile views, then gather the approved copy, icons, images, fonts and colour or spacing tokens.
- Create a new React project in Lovable or your existing repository, and record the required framework, TypeScript setting, routing approach, component library, browser support and API constraints.
- Paste the supplied prompt with the screenshots, design tokens, assets, content and any API shapes attached, then ask for the file tree and implementation rather than a visual mock-up.
- Run the generated project locally or in the tool's preview and compare every screen against the design at the supplied viewport sizes, checking layout, typography, assets and responsive changes.
- Exercise each visible interaction, including navigation, forms, menus, loading, empty and error states, and record any mismatch with the exact screen and expected behaviour.
- Ask the model to change the named files for those mismatches, then run the same comparison again without accepting unrequested changes to content or behaviour.
- Ask a frontend colleague to inspect the code and test keyboard navigation, focus states, semantic labels, error handling, dependency security and real-data integration before merging or deploying it.
Prompt
Turn the attached UX design into a React implementation. Context: - Product or feature: [describe it] - Intended users: [describe them] - Required stack and constraints: [for example, React with TypeScript, existing component library, routing, API constraints] - Target browsers and screen sizes: [list them] - Available assets: [attach or list logos, icons, images and fonts] - Content and data: [paste the approved copy, example data or API shapes] First, identify the screens, reusable components, user actions, loading states, empty states, error states and responsive behaviour shown or implied by the design. Separate facts taken from the design from assumptions. Do not invent brand assets, product behaviour, copy or API responses. Ask concise questions only where a missing decision blocks implementation. Then provide: 1. A file tree. 2. Complete React and TypeScript code for the first usable implementation. 3. Styling that matches the design, including spacing, typography, colour, layout and responsive states. 4. Keyboard access, visible focus states, sensible semantic HTML and labels for controls. 5. Mock data or clearly marked placeholders where real services are not supplied. 6. Run and install instructions for the stated stack. 7. A short list of assumptions, known gaps and manual checks. Keep components reusable rather than putting the whole screen in one file. Explain any design decision that cannot be determined from the supplied material. After the first implementation, wait for my visual and behavioural feedback and change only the relevant files.
Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.
What it gets wrong
- AI cannot recover interaction rules, edge cases or business constraints that the design never records.
- It produces plausible component boundaries, but it cannot know which patterns fit your existing codebase without inspecting and understanding that codebase.
- It cannot reliably judge whether spacing, motion, accessibility and responsive behaviour are right for your users without structured testing.
- It does not take responsibility for security, privacy, accessibility or defects after the React code is deployed.
- A generated interface can look finished while its real API states, permissions and failure handling are still missing.
What caps this at PARTLY: verification cost, judgement under ambiguity and context depth.
How we scored this
Five axes, each scored nought to two by hand: ten means AI carries the task cleanly, and the thresholds that turn a total into YES, PARTLY or NO are published in the methodology. Each axis name links to its definition.
| Axis | Score (0–2) |
|---|---|
| Output | 2 |
| Inputs | 1 |
| Verification | 1 |
| Liability | 1 |
| Effort delta | 2 |
| Total | 7 / 10 |
The methodology and its thresholds are published in full.
FAQ
- Can AI convert a Figma design into React?
- Yes, it can generate a useful React starting point from screenshots, exported assets and design details. It will not reliably infer every interaction, responsive rule, data state or constraint from a Figma file, so a developer still needs to specify and check those parts.
- What do I need to give AI to turn my design into React?
- Give it screen exports, mobile and desktop states, approved copy, assets, design tokens, interaction rules, API shapes and the constraints of your existing project. Include loading, empty, error and permission states, because polished screens alone do not describe a working product.
- Can AI-generated React code go straight into production?
- Not without a technical review and testing. Check the rendered screens, keyboard access, responsive behaviour, dependencies, security, real-data handling and error states before merging or deploying the code.
- Which AI tool can turn my design into a React app?
- Lovable is a suitable purpose-built option for turning a detailed product description and supplied design material into a working full-stack app. Its output still needs to be compared with the source design and reviewed in your project's technical context.
Nearby answers
Assessed by gpt-5.6-luna (gpt-5.6-luna) on 2026-08-13, second-checked by an independent model. Wrong somewhere? Email [email protected] and it gets re-checked.
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