For years, the gap between UI design and frontend development has been a primary source of friction in product development. Designers envision an experience, and developers manually translate that vision into functional code, a process filled with back-and-forth and manual effort.
At its I/O 2025 conference, Google introduced Stitch AI, a new experimental tool from Google Labs that aims to create a more fluid and integrated workflow.
This isn't just another wireframing tool. It's an AI-powered UI generator built on the multimodal capabilities of the Gemini 2.5 Pro model. But does it live up to the hype? This in-depth review focuses on its practical usefulness, testing the iterative workflow required to get from a vague idea to a polished, coded UI.
Stitch is a text-to-UI and image-to-UI tool that operates on a simple premise: you describe an interface, and it builds it. Its core function is to produce two critical outputs simultaneously:
As Google's team puts it, Stitch was built to "turn simple prompt and image inputs into complex UI designs and frontend code in minutes." It accepts text, sketches, or even screenshots as input. This design-to-code-parity is its central value proposition, aiming to eliminate the manual, error-prone process of translating a static design file into a functional frontend.
Stitch is not a mind-reader, but it makes intelligent assumptions. Its true power is unlocked through iterative refinement. We ran a real test to see how it handles a vague prompt and subsequent refinements.
Step 1: The "First Draft" (The Vague Prompt)
We began with a low-effort, generic prompt to see what Stitch would produce.
Prompt: "make a project management dashboard"
Result: The result was surprisingly specific and high-quality. Instead of a generic web layout, Stitch intelligently defaulted to a mobile-first UI and generated a clean, component-based dashboard. It included logical sections like "Tasks Due Today," "Overdue," "My Tasks," and "Active Projects" with progress bars.
Key Takeaway: Even a vague prompt can produce a strong, structured starting point. Stitch makes smart assumptions and understands the implied components of a "project management dashboard."
Step 2: Iterative Refinement (Styling in Dark Mode)
The first draft is functional, but we need to apply a specific brand and style. We used a follow-up prompt to refine the generated design.
Follow-Up Prompt: "This is a great start. Now, change the entire theme to a professional dark mode. Make the primary accent color a vibrant electric blue, and apply it to the floating '+' button, the active 'Dashboard' icon in the bottom nav, and the 'Progress' bars."
Result: Stitch executed the prompt perfectly. The entire application was re-skinned with the new dark mode theme, and the electric blue accent color was correctly applied to the floating action button, the active navigation icon, and the progress bars.
Key Takeaway: The iterative workflow is seamless. You can make broad stylistic changes (like "dark mode") or specific component-level edits using natural language, and Stitch will intelligently apply them.
Step 3: Functional Refinement (Adding a New UI Element)
The dashboard is styled, but it's static. We tested if Stitch could add a new, contextual UI element. The + button implies "add task," so we prompted for the UI that should appear when it's clicked.
Follow-Up Prompt: "This looks perfect. Now, when a user clicks the blue '+' button, generate a pop-up modal for adding a new task. The modal should have a title 'New Task', a text input field for 'Task Name', a dropdown for 'Project', and a 'Create Task' button. Keep the dark mode theme."
Result: Stitch correctly understood the context. It generated a new pop-up modal over the existing dashboard, matching the dark mode theme and including the specific form elements requested.
Key Takeaway: This step confirms the AI's contextual awareness. It didn't just create a new screen; it understood what a "pop-up modal" is and how it should function in relation to the existing UI.
This 3-step process, which takes only a few minutes, ends with two powerful export options:
After testing the workflow, it's clear that Stitch is not a "job-killer." It's a "job-automator" for the most tedious parts of the process.
Here are the answers to the most common questions about Stitch.
1. Is Google Stitch AI free? Yes. As of its 2025 release, Stitch is an experiment in Google Labs and is currently free to use.
2. What's the difference between Stitch AI and Figma? They serve different purposes. Stitch generates UI from a prompt (text or image). Figma is a manual design tool used to create and refine UI. The best workflow is to use Stitch to generate your first draft and then use its "Paste to Figma" feature to do your professional refinement in Figma.
3. Does Google Stitch AI replace designers? No. Stitch is an assistant that handles the repetitive, time-consuming tasks of initial layout and coding. It does not replace the need for a professional designer to define user experience, create a unique brand identity, and make strategic decisions about user flow and information architecture.
4. What code does Google Stitch AI export? Stitch exports clean, functional HTML and Tailwind CSS. This code is production-ready and can be copied directly into any web project.
Final Conclusion
Google Stitch AI is a genuinely useful tool that signals a major shift in the software development lifecycle. It successfully bridges the gap between design and code, automating the mundane tasks that slow down innovation.
The era of manually translating static design files into code is ending. The future is a collaborative workflow where designers and developers guide powerful AI assistants to execute their intent. Stitch is one of the first and most practical tools for this new reality.
You can try it now at the Google Labs website.
