Is Figma Make ready for dev-handoff?

Design-to-code tools that generate clean code.

Unless you’re a “unicorn,” front-end development is typically out of a UX designer’s wheelhouse. We rely on our developers to implement our designs using clean, usable code — preventing tech debt, high maintenance costs, and poor performance.

But implementing designs takes time, especially as a product grows in complexity and many cross-functional teams are involved. Some tools address this issue, such as design systems and agile methodologies, but the development process is still far from being fully optimized.

This is where vibe coding, low-code/no-code tools, and AI assistants come into play. They empower both UX designers and developers to bypass repetitive tasks and quickly generate functional demos and staging environments.

Figma recently unveiled its version of AI-powered design-to-code feature, Figma Make. Instead of handing off designs to your developers to start from scratch, you can prompt the AI assistant to generate code based on your requested functionality.

To a designer’s eye, Figma Make seems it could drastically reduce the development process. While Figma Make may not be intended for production-ready code, designers are turning to AI tools to simplify handoff. But if the code output isn’t clean or usable by developers, can it improve the design-to-dev workflow?

Let’s elaborate on why clean code is so critical, look closer at Figma Make’s pros and cons, then review alternative design-to-code tools to determine which is best for your designs and developers.

Isn’t code “clean” if it works?

You might think that if code runs without errors, it must be “clean.” For code to be “clean,” its performance and functionality matters just as much as its readability and quality. It’s like buying a used car; maybe it runs fine today, but there are many hidden problems under the hood that will only hurt you in the long-run.

But messy code doesn’t come out of nowhere. It’s often due to time pressure, unclear requirements, and team misalignment. If designers understand why messy code happens and its potential risks, they can better collaborate with developers to avoid contributing to cluttered code.

Common causes of messy code

• Tight deadlines: When speed is prioritized over quality, developers may “duct tape” solutions for the code to work now. It may seem like a shortcut, but it creates fragile code predisposed to errors and breaking.
• Fluctuating requirements: If feature or product requirements are constantly changing, developers will be unclear on functionality. This causes the team to build and rebuild certain components — leading to less time to organize the code.
• Absolute positioning and auto-layouts: When elements are placed using absolute positioning or are buried in many frames, the code prioritizes style over meaning. Elements wrapped in extra <div> containers look correct, but are functionally wrong.

Example of unnecessary <div>encasing <ul>:

<div id="topNav">
  <ul id="bigBarNavigation">
    <li><a href="/">Home<a/></li>
    <li><a href="/contact">Contact us<a/></li>
    <li><a href="/about">About us<a/></li>
  </ul>
</div>

Consequences of messy code

• Poor accessibility and SEO: Without proper, semantic HTML, like using <h1> to define the webpage’s main heading, assistive technologies (e.g., screen readers) and search engines can’t process the content as intended.
• Low readability and collaboration: Developers struggle to read cluttered code–especially those who didn’t originally write it. If a developer can’t understand the code’s purpose or function, they’re blocked from maintaining or collaborating on the code.
• Prone to bugs and errors: Not only is the code low-quality from its unclear structure, it’s more difficult to debug when an issue arises. As new features or enhancements are introduced, the code becomes susceptible to “breaking.”

A look under Figma Make’s hood

Figma Make is Figma’s new, AI-powered feature that claims to bridge the gap between design and code. With user-generated prompts, Figma Make creates interactive UI prototypes directly from your Figma designs. These AI-created prototypes significantly reduce the time needed to create complex prototypes.

On the surface level, Figma Make generates prototypes effortlessly. But is it too good to be true? Under the hood, things get more complicated when you take a look at its code output.

What Figma Make does well

• Quick, AI-generated prototypes: Designers can prompt to “Make it work,” instantly turning static mockups into interactive prototypes to validate concepts or test with users.
• Figma integration: Since Figma Make is a Figma feature, designers can go straight from design to code without opening a new app or plugin — making the iteration process frictionless.
• “Point and edit” output: Designers can tweak the generated code by selecting elements in “Preview” and asking for changes, like replacing images or adjusting spacing, without creating a completely new prompt.

Where Figma Make struggles

• Messy code structure: Figma Make relies on <div> elements instead of using semantic HTML tags like <header> or <nav>. The code visually works, but lacks meaning and structure for accessibility and SEO.
• Page layout limitations: Long pages with scrollable content are often skipped in the AI-generated prototype. So more complex mockups with content “below the fold” are less likely to have complete prototypes or code exports.
• Developer frustration: Figma Make’s code output isn’t near production-ready. Exported code must be restructured and debugged by developers — making it more costly than starting from scratch.

Alternative design-to-code tools

You probably think AI assistants accelerate design implementation, but they can actually slow developers down. A recent research study by Model Evaluation & Threat Research (METR) found it took experienced developers up to 19% longer to complete tasks when using AI tools like Claude. The delay was due to the majority of the developers reading every line of code and making “major modifications” before accepting the AI output.

Are AI tools like Figma Make worth it?

For now, Figma Make is best for prototyping concepts or getting quick stakeholder feedback; not for generating production-ready code. If designers rely on it for developer handoff, they risk wasting developer time as they read, rewrite, or delete most of the AI output.

But are there alternatives to Figma Make that generate clean, reliable code? Let’s review Anima App and Builder.io to see how they compare to Figma Make.

Demo comparison

To test and compare each tool, I designed a simple website for a hypothetical company in Figma. The website includes 4 pages: the home screen, about us, pricing, and contact us pages. I asked each tool to connect each page and make a working prototype for the website.

For each tool, I compared the AI-output for the following: semantic HTML, design accuracy, prototype functionality, and customizable framework.

I. Figma Make

At first, I was confused on how to connect my designs to Figma Make since my mockups were in a design file. There’s no “Copy to Figma Make” action like there is for Figma Sites. I realized I needed to copy the frame in my design file and paste it in the AI prompt in my Figma Make file.

When migrating my designs to Figma Make, I was only able to copy/paste 3 frames at a time. I wasn’t able to paste the “Contact us” page, so Figma Make generated designs on my behalf. It’s cool it did this automatically, but the compromise is frustrating if I need specific input fields or UX copy for legal purposes.

Results:

• Semantic HTML: Figma Make heavily relies on <div> containers. These generic elements aren’t helpful to assistive technologies because they don’t include semantic meaning, like if an element is a button or text field.
• Design accuracy: Figma Make mismatched content intended for specific sections and pages. For example, it completely replaced or skipped imagery in the content. Though it kept typography styles and colors consistent, it ignored padding and margins from the designs.
• Prototype functionality: Figma Make inherently understood the site map and properly linked each page to the header component. It also created components that had consistent state usage, like on-hover.
• Customizable framework: Figma Make allows you to upload a library for it to reference, but you have to add explicit guidelines for it to follow — making it tedious to get what you want.

II. Anima App

Anima App’s Figma plugin generates code for your frames straight from your Figma design file. After running the plugin in Figma, I selected the frame I want to prototype and get code for.

Because I added a few interactions to connect the screens together, Anima recognized the frame was a part of the flow and prompted me to select the screens I wanted included in the export.

I picked my preferred framework (or you can keep the default framework), then selected “Prompt in Playground” to be taken to Anima’s desktop website.

Results:

• Semantic HTML: Anima uses semantic HTML tags to distinguish interactive elements, like the site’s header and buttons. This benefits screen reader users because these elements will be correctly recognized and announced by the screen reader.
• Design accuracy: Anima kept the content order and imagery used much better than Figma Make. In some sections, the alignment was out of place, but would require far less AI-prompts to fix the output to make it 1:1 with the designs.
• Prototype functionality: Though you do need to create the prototype in Figma for Anima to recognize the flow, you have control over the interactions so the AI-output doesn’t have unintended interactions between screens.
• Customizable framework: Anima allows you to select React, Vue, or HTML, as well as pick between UI libraries and languages; the default selection with React and Shadcn components produces reliable prototypes.

III. Builder.io

Builder.io’s Figma plugin also generates code and prototypes directly from your Figma designs. After I ran the plugin in my Figma Design file, I selected the 4 frames I wanted included in my prototype and code output.

After selecting “Export” from the plugin, it prompts you to copy the design export to paste it in Builder’s desktop website. Before you send the message, you can also customize the framework and MCP server (for access to external systems).

Results:

• Semantic HTML: Builder does use semantic HTML tags for interactive elements, but encases each of them in a <div> container. This container is unnecessary and can lead to bad accessibility.
• Design accuracy: Builder completely changed my content; it replaced images, added copy, and changed the color palette. It felt like Builder just took the general concept and did what it wanted with little reference to my designs.
• Prototype functionality: Though the designs aren’t 1:1 with what I gave Builder, the overall prototype has clean interactions and flows correctly between the website’s pages. Also, I didn’t tell Builder how to connect the screens, and it correctly assumed the flow.
• Customizable framework: Builder allows you to select both the framework and the MCP server if you have a paid plan. With the free version, React is the default.

Final verdict

Figma Make seems promising at first glance, but isn’t ready for serious dev-handoff quite yet. Though its AI-generated prototypes are quick and well-integrated in Figma’s ecosystem, the code output is messy and uses generic elements like <div>. For now, Figma Make’s prototypes should be used for concept validation and user testing.

A design-to-code alternative to consider is Anima App. Not only was the AI-output more 1:1 to my designs, the code used semantic HTML tags and was more clean.

As AI design tools evolve, it’s important they actually provide value–especially when it comes to developer handoff. Though Figma Make has taken a step in an exciting direction, it’s not a reliable solution for production-ready code yet. But tools like Anima App show more potential for truly bridging the gap between design and development.

Is Figma Make ready for dev-handoff? was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

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