From Figma to Frontend: A Practical Guide to AI-Powered Code Generation

This is the story of how we streamlined design-to-code handoff using Locofy, Builder.io, and Figma MCP, followed by lessons learned for other teams. Our goal was to establish a clear process for converting Figma designs into high-quality, maintainable code. After testing several tools, we settled on three distinct approaches:

• Full Figma-to-Code Platforms: Tools like Locofy and Builder.io that convert entire designs.

• IDE Integrations: Using Figma MCP to feed design context directly to AI assistants in our code editors.

• AI-Driven Prototyping Tools: Like V0, which bridges design and development.

We will walk you through what works, what doesn't, and how to leverage these tools to speed up development and improve the quality of your final products.

The Landscape: What’s Out There?

AI-powered design-to-code tools generally fall into two main camps: platforms that generate entire pages from a design file, and integrations that enhance the tools you already use. We evaluated several methods and found clear winners for different scenarios.

Full Figma-to-Code Tools

These platforms are built to take a complete Figma design and turn it directly into frontend code. We focused on two leaders in this space:

• Locofy: An AI-powered tool that uses Large Design Models (LDMs) to generate clean, modular, and responsive code. It's powerful but comes with a steeper price tag.

• Builder.io: A comprehensive visual development platform that combines design-to-code with a CMS and visual editing. Its standout feature is the ability to refine code with AI prompts before exporting, making it incredibly flexible.

IDE Integrations (Figma MCP)

The Model Context Protocol (MCP) creates a direct link between Figma and AI coding assistants inside an IDE like VS Code or Cursor. This gives the AI agent deep design context for far more accurate code generation than prompting with screenshots. We found this approach perfect for surgical enhancements to existing projects.

What About V0?

It's important to clarify that we use V0 extensively in our processes, particularly for rapid prototyping and generating UI from scratch using text prompts. It excels in that area.

However, for the specific task of converting existing Figma designs, we focused on its direct integration. In our tests, we found this particular feature performed poorly. Combined with privacy concerns—as our data could be used for model training—we decided not to pursue its Figma-to-code capabilities as part of this workflow.

Choosing the Right Strategy: Project Scenarios

The right tool depends entirely on your project's lifecycle stage. There’s no single best solution, only the best fit for the job at hand.

Starting from Scratch

For new projects, the goal is to generate as much foundational code as possible. Meticulously prepare your Figma design so tools like Locofy and Builder.io can create the initial scaffolding. This output isn't final, but it’s a massive head start that you can then bring into your IDE for componentization, accessibility improvements, and logic integration.

Working with an Existing App

In ongoing projects, a full-page generation is overkill and often impractical. This is where Figma MCP servers are invaluable. This surgical approach lets a developer generate code for a specific component or view and integrate it into an established codebase without a full rewrite.

Your Quick Decision Guide

• Building a new app from a clean design? → Use a full design-to-code tool like Builder.io or Locofy.

• Adding a new component to an existing app? → Use Figma MCP to enhance your AI assistant in the IDE.

Real-World Comparison: Builder.io vs Locofy

Locofy: The High-Fidelity Generator

• Best For: Rapidly scaffolding new projects with complex, well-defined designs. Excellent for generating a strong baseline of responsive components.

• Cost-Benefit Analysis: Locofy uses a token-based model, where a complex screen can cost 200-300 tokens (80-120$). While this seems expensive, if it saves 8-10 developer hours on initial setup, the cost can be justified. It's an investment in speed.

Builder.io: The Versatile Middle Ground

• Best For: Projects where some in-tool iteration might be needed before exporting code. Its AI prompting feature allows for refinement of the generated UI directly.

• Key Feature: The ability to refine code with AI prompts before export makes it a flexible "middle ground." You can ask it to "refactor this into smaller components" or "use semantic HTML tags" and see the results instantly.

Figma MCP: The Surgical Enhancement Tool

• Best For: Adding small, well-defined components or views to an existing project. Perfect for when you don't want to generate a whole page, just a single piece.

• Key Feature: It enhances the AI tools we already use (Cursor, Copilot). It provides rich, accurate design context directly in the IDE, resulting in much higher-quality code snippets than prompting with screenshots.

Locofy vs Builder.io in Practice

What We Found

• Code Quality: Locofy claims to produce "production-grade" code, but we found that both its output and Builder.io's required manual refactoring. The initial generation from Locofy was slightly cleaner, but not enough to justify the significant price difference.

• Workflow: Builder.io's AI prompting feature is a game-changer. The ability to ask it to "refactor this into smaller components" or "use semantic HTML tags" before exporting is a genuine advantage that saves significant time. Additionally, its Command Line Interface (CLI) offers a powerful middle ground; we can use it directly within an existing project, giving the AI agent full context of our codebase to implement the design.

• Pricing: This was the deciding factor. A complex screen in Locofy can cost $80–$120 in tokens. A similar screen in Builder.io costs around $3–$5. For our typical use case of generating one or two key screens, Builder.io is the far more practical choice.

Our Conclusion

For smaller projects and iterative workflows where flexibility is key, Builder.io is our go-to. Its combination of a flexible workflow and predictable, low cost makes it a clear winner for our needs. We might consider Locofy for a large-scale project where we need to generate an entire component library from a perfectly structured design, but the cost will always be a major consideration.

Designer Best Practices: Preparing Figma for Flawless Handoff

Here’s the most important takeaway: the AI is only as good as the design it's reading. A well-structured Figma file is the single most critical factor for success. These practices, inspired by the Locofy docs, are essential for any design-to-code tool.

1. Group Layers Logically

• Problem: Messy, ungrouped layers translate into a messy DOM with endless nested <div>s.

• Best Practice: Use Figma Frames (F key) instead of simple Groups (Cmd/Ctrl+G). Name your frames semantically (e.g., UserProfileCard, HeaderNavigation). Do not leave dozens of ungrouped layers named Rectangle 5 or Group 21.

2. Apply Auto Layout Everywhere

• Problem: Manually positioned elements are unresponsive and generate fragile, absolute-positioned code.

• Best Practice: Auto Layout is Figma's version of CSS Flexbox—use it for everything. Do define direction, spacing, and padding. Do not manually drag elements to position them.

3. Ensure Consistent Spacing

• Problem: Inconsistent spacing creates messy, arbitrary inline styles in the final code.

• Best Practice: Do use Auto Layout's "gap" and "padding" properties with your established spacing tokens (e.g., 4, 8, 16px). Do not create space by drawing invisible rectangles.

4. Create Reusable Figma Components

• Problem: Copy-pasting the same group of layers leads to duplicated code.

• Best Practice: The AI will recognize Figma Components as reusable code components. Do turn every repeating element (buttons, cards, inputs) into a component. Do not just copy and paste the same design elements multiple times.

5. Use Clear, Semantic Layer Naming

• Problem: Default layer names like Frame 3489 become meaningless variable or class names.

• Best Practice: Do name layers based on their function (e.g., submit-button, user-avatar). This often translates directly to variable names and improves accessibility.

Developer Workflow: Turning AI Output into Production Code

It’s crucial to treat AI-generated code as a starting point, not a final product. Our workflow ensures we use it as an accelerator, not a crutch.

Step 1: Pre-flight Check (Project Setup)

Before generating anything, prepare your local environment. Set up your Next.js or Nuxt project and configure your tailwind.config.js with the project's custom fonts, colors, and variables. This helps the tools generate code that aligns with your design system from the start.

Step 2: Generation (Choosing Your Tool)

• New Project: Use Locofy or Builder.io on the most representative views of the application. Don't try to generate everything. Focus on pages that establish the main layout and contain common components.

• Existing Project: Use Figma MCP by selecting a frame in Figma and feeding its link into a Cursor or Copilot prompt within your IDE.

Step 3: Initial Code Integration

Get the generated code into your project. For Locofy, this is usually a ZIP file. For Builder.io, you can use their CLI. Review the file structure and place the components where they belong.

Step 4: The AI Refactoring Pass

This is where the magic happens. Use an AI assistant like Cursor to elevate the generated code.

TIP: The first AI refactoring pass usually gets us 80% of the way to production-ready code — don't skip it.

Example Refactoring Prompt for Cursor:

Review the following code generated from a Figma design. Your task is to refactor it into clean, production-ready code.

Componentize: Break this down into smaller, reusable React components. Identify logical boundaries.

Add Props: Make the components dynamic by adding props for things like text, image URLs, and event handlers.

Improve Accessibility: Ensure all interactive elements are buttons or links, images have alt tags, and appropriate ARIA roles are used.

Cleanup Styling: Ensure it uses Tailwind CSS utility classes effectively and consistently. Remove any redundant or messy styles. Use already defined variables.

IMPORTANT: Do NOT change the visual appearance or layout. The final result should look identical to the original code.

Step 5: Manual Refinement and Logic Integration

The AI refactoring gets you most of the way there, but the final 10-20% is always manual. This is where you connect components to state management, hook up event handlers, and perform a final code review for quality and adherence to your team's standards.

How Builder.io and Locofy Perform with Figma Files

To put these tools to the test, we ran them on both our own internal projects and the optimized test files provided by Locofy, using the same meticulously prepared Figma file for each to ensure a fair comparison. The results were consistently strong, but this success was heavily dependent on the quality of the source design. Because our Figma file was already well-structured—with logically grouped and named layers, Auto Layout applied, and reusable components—both Locofy and Builder.io were able to correctly interpret the design, generate reusable code components, and use semantic HTML.

Builder.io offers two methods of use: a web application, similar to V0, and a command-line interface (CLI). Both methods yield comparable results. The CLI offers the benefit of providing the AI agent with repository context, enabling direct implementation of the view within the application.

Our Final Recommendations

AI-powered code generation is not magic—but when paired with well-structured Figma designs and a solid development workflow, it becomes a serious productivity boost.

1. Prioritize New Projects for Full Design-to-Code Tools: Tools like Builder.io offer the most value when working from clean Figma files in greenfield projects. Use them to scaffold your base, then refine.

2. For Existing Apps, You Have Options: When enhancing an established codebase, Figma MCP is best for smaller components. For adding larger views or features, Builder.io's CLI is a powerful alternative, as it allows the AI to work directly within your project's context.

3. Builder.io vs. Locofy: Choose Based on Versatility and Budget: Use Builder.io for most projects. Its flexibility, CLI, and lower cost make it suitable for both new and existing applications. Consider Locofy only for large, complex UIs where its high-fidelity output might justify the token-based pricing.

4. A Well-Prepared Figma File is Everything: The single biggest factor in code quality is the design input. A designer who understands these principles is your most valuable asset in this workflow.

These tools won’t replace developers or designers, but they will help both teams move faster, communicate better, and ship cleaner code.