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As of 13 August 2026, AI can turn your prototype into HTML and CSS.
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 neededchat-fluent
Who has to check ityou
What the alternative costsLovable is an AI tool that builds a working full-stack version from a description.
If this goes wrong: the page looks close to the prototype but breaks at a screen size or misses an interaction, so you fix the generated files before publishing.
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.
Do it yourself
Second choiceA chat interface, chat-fluent skill, and roughly 1 hour until you can act on the result.
How to actually do it
- Open the prototype and export each relevant screen at the main desktop and mobile sizes, then gather the exact copy, images, icons, fonts and interaction states.
- Create a new project in an AI coding tool such as Bolt and attach the exported screens and assets, or paste a shareable prototype link if the tool can access it.
- Paste the supplied prompt and fill in the target page, screen sizes, interactions, brand rules and technical constraints from the prototype.
- Run the generated project locally or in the tool's preview and compare each page against the corresponding prototype screen at the supplied sizes.
- Test every listed interaction with a mouse and keyboard, including focus states, forms, menus, validation, loading, empty and error states, and record each mismatch.
- Paste the mismatch list back into the tool and ask it to change only the affected HTML, CSS or JavaScript without replacing the supplied content or assets.
- Open the final files in the intended hosting or build environment, check the browser console and test the page again on desktop and mobile before sending it to your developer or publishing it.
Prompt
Turn the prototype described below into a responsive, accessible web page using semantic HTML and separate CSS, with JavaScript only where it is needed for the stated interactions. Prototype source: [paste a public prototype link, or attach screenshots or exported frames] Target page or feature: [describe the page] Required interactions and states: [list clicks, menus, forms, validation, loading, empty and error states] Target screen sizes: [list desktop, tablet and mobile widths if known] Assets: [attach or list images, icons and fonts] Content: [paste the exact text to use] Brand rules: [colours, type scale, spacing, border and button rules] Technical constraints: [framework, browser support, file structure or hosting constraints] First, list any details that are missing or ambiguous and state sensible assumptions without inventing product requirements. Then produce the complete files in clearly labelled code blocks. Use reusable classes, CSS custom properties, keyboard-accessible controls, visible focus states, useful alt text and responsive layouts. Do not replace supplied copy with filler. Do not claim that the result matches the prototype until it has been checked. Finish with a short manual test list covering every interaction, mobile layout, keyboard navigation, focus visibility, overflow and obvious console errors.
Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.
Use a tool built for this
The distant third
What it gets wrong
- It cannot infer missing product requirements from a static screen, so unshown states and interactions remain your decisions.
- It often approximates spacing, typography, icons and responsive behaviour rather than reproducing the design precisely.
- It cannot reliably judge whether the generated structure is maintainable in your existing codebase without access to that codebase and its conventions.
- It does not replace a proper accessibility, browser and performance check before release.
Even on a YES, the friction has a name: verification cost 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 | 2 |
| Verification | 1 |
| Liability | 2 |
| Effort delta | 2 |
| Total | 9 / 10 |
The methodology and its thresholds are published in full.
FAQ
- Can ChatGPT convert my Figma prototype to HTML and CSS?
- Yes, if you provide accessible frames, screenshots or exported assets and describe the interactions and responsive states. It can generate a strong first implementation, but you still need to compare it with the prototype and test the page in a browser.
- Can AI make HTML and CSS from a screenshot?
- Yes. A screenshot is enough for a visual approximation, but it does not show hidden states, semantic structure, keyboard behaviour or responsive rules, so those need to be supplied and tested separately.
- Will AI-generated HTML and CSS match my prototype exactly?
- Not reliably on the first attempt. AI can get the layout and styling close, but fonts, spacing, breakpoints, icons and interaction states need comparison against the prototype and targeted corrections.
- What is the best AI tool to turn a prototype into a website?
- Bolt is a suitable choice when you want an AI web development agent to build and deploy an app in the browser. Framer is another option for AI-assisted site design that publishes a production website, especially when the result is a marketing site rather than a custom application.
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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