Imagine we have a solid app idea. The logic is clear, the tech stack is decided, and the team is ready to build. But then comes the question no one wants to answer: “Who is handling the design?” Suddenly, the momentum stalls. A designer needs to be looped in, Figma files need to be created, feedback rounds begin, and what should have taken a day stretches into a week.
UI design has quietly been one of the biggest bottlenecks in product development, and it affects everyone: developers who just want to ship, founders who cannot afford a dedicated designer, and product managers waiting on mockups before a single line of code is written.
Announced at Google I/O in May 2025 and made publicly available in early 2026, Google Stitch is one of the most consequential releases in the generative AI design space this year. In this blog, we will explore what Stitch is, walk through building a real e-commerce UI with it from scratch, understand how it compares to existing tools, and decide where it belongs in a modern development workflow.
What is Google Stitch?
Google Stitch is an AI-powered UI design agent that generates production-ready user interfaces and their corresponding code from a plain-text description or a rough sketch.
In practical terms, we describe what we want, in plain language, and Stitch produces a complete, styled UI design along with the code to implement it. No Figma skills are required. No design system setup. No back-and-forth between designer and developer.
Stitch lives under Google Labs, Google’s experimental products division. It was announced as part of Google I/O 2025 and made publicly available in early 2026. The tool is entirely browser-based, requiring no installation and no paid subscription to get started.
It is designed for a wide range of users, including frontend developers who want a design head start, UI/UX designers looking to accelerate the ideation phase, startup founders who need a working prototype quickly, and complete beginners who have never opened a design tool in their lives.
What does Stitch actually offer?
Before walking through the hands-on example, it is worth understanding the full scope of what Stitch provides. The feature set is broader than a single-prompt design tool.
- Text-to-UI generation: The most fundamental capability is text-to-UI generation. We type a description of a screen and Stitch renders a complete, styled UI as an editable layout tied directly to exportable code. This alone handles the most common use case, but the features surrounding it are what make Stitch a genuine workflow tool rather than a novelty.
- Image-to-UI generation: Stitch also supports image-to-UI generation. We can upload a hand-drawn sketch, a whiteboard photo, or an existing screenshot, and Stitch will interpret the visual structure and generate a polished, production-ready UI from it. This is particularly useful in early brainstorming sessions where ideas live on paper before they reach a screen.
- Multi-screen generation: A standout workflow feature is multi-screen generation. Rather than designing one screen at a time and hoping the results stay consistent, we can prompt Stitch to generate multiple connected screens in a single session. Stitch maintains the same typography, color palette, and component style across all of them, automatically.
- Code export: When it comes to exporting, Stitch supports code export across multiple frameworks, covering both web and mobile targets. Whether we are building a web app or a native mobile app, the generated code is intended to slot directly into an existing project with minimal rework.
- Figma export: For teams that work in Figma, Stitch offers a Figma export option. The generated UI can be transferred into a Figma file ready for annotation and refinement without manual recreation.
- Voice-based interaction: Stitch also incorporates voice-based interaction, powered by the native audio capabilities of Gemini 2.5 Flash. Rather than typing every edit, we can speak instructions and have Stitch respond to them, making rapid iteration feel more conversational.
Hands-on: building a mini e-commerce UI with Stitch
The best way to understand Stitch is to build something with it. We will walk through creating a three-screen e-commerce UI, covering a product listing page, a product detail page, and a cart page, using only natural language prompts. The goal is to go from nothing to a coherent, exportable UI in under ten minutes.
Getting started
We navigate to stitch website and sign in with a Google account. The interface lands us directly in a prompt-driven canvas where we can start describing a UI immediately. It is worth noting that earlier versions of Stitch included a Standard and Experimental mode toggle, but this was removed as part of the March 18, 2026 update, which streamlined the interface into a single unified experience.
Screen 1: Product listing page
We start with the first screen of our sneaker store. In the prompt bar, we enter the following:
Design a clean product listing page for a sneaker store.
Show a grid of product cards with image, name, price, and
an Add to Cart button. Light theme, modern feel.
Within seconds, Stitch renders a full product listing page. The output includes a card grid, clean typography, and a consistent spacing system throughout. The Add to Cart buttons are styled as primary action elements, and the overall layout reflects what we would expect from a professionally designed e-commerce interface.
What Stitch gets right here is visual hierarchy. The product image commands attention, the name and price follow in a natural reading order, and the action button sits at the bottom of each card with appropriate visual weight. If we want to adjust anything at this stage, such as switching to a dark theme, changing the card layout to a single column, or adjusting the button style, we type that instruction directly and Stitch updates the design in place.
Screen 2: Product detail page
For the second screen, we continue in the same session with a follow-up prompt:
Now create the product detail page for one sneaker.
Include a large product image, size selector, color options,
rating, and a Buy Now button.
We did not need to repeat the context from Screen 1. Stitch carries the visual language of the session forward, applying the same font, button style, and spacing rhythm throughout. The detail page renders with a prominent hero image section and a five-star rating component and reviews. However, instead of a size selector, color options and a Buy Now button, it simply added Details and Checkout button which show it sometimes overlook the instruction but we can always edit it which makes it easy handy.
This cross-screen consistency, maintained without being explicitly instructed, is what separates Stitch from a one-shot generation tool. It is treating the UI as a system, not a collection of isolated screens.
Screen 3: Cart and checkout page
We generate the final screen with the following prompt:
Generate a cart page showing 2–3 items, quantity controls,
price breakdown, and a Proceed to Checkout button.
The cart page arrives with items. Each cart item displays a thumbnail, product name, size, a quantity stepper control, and a remove option. It also breaks down the subtotal, ending with a Checkout button that matches the primary button style established across the previous two screens.
Three screens. One coherent product. Under five minutes of prompting.
Exporting the design
Once we are satisfied with the screens, Stitch offers two clear paths forward. Designers can use the Figma export, which pushes all three screens into a Figma file with named layers, auto-layout components, and grouped elements ready for annotation and refinement. Developers can select a target framework such as Tailwind CSS, and Stitch generates the corresponding code for each screen.
This is where Stitch hands off to the rest of the workflow. It generates a strong starting point and then steps aside.
DESIGN.md: Stitch’s design specification file
When we export from Stitch, we are not limited to visual assets or framework code. Stitch also generates a file called DESIGN.md, and it is one of the most underappreciated parts of the tool.
DESIGN.md is a structured, human-readable design specification file that captures the decisions behind a generated UI, including component structure, spacing values, color tokens, typography choices, and layout logic.
Think of it as a design brief written in plain text. A developer reading it immediately understands not just what the UI looks like, but why it is structured the way it is. More importantly, AI coding agents can read it too. If we drop DESIGN.md into a tool like Claude Code, Cursor, or GitHub Copilot, the agent can implement the design faithfully without having to infer intent from the visual output alone.
This is where Stitch starts to feel less like a design tool and more like a design protocol. The generated UI is the output for humans. DESIGN.md is the output for machines. Together, they create a bridge between the design phase and the development phase that has historically required significant manual effort to cross.
MCP integration: Connecting Stitch to your AI workflow
Alongside DESIGN.md, Stitch’s support for MCP (Model Context Protocol) is the other feature that distinguishes it from earlier AI design tools.
MCP (Model Context Protocol) is an open standard, introduced by Anthropic, that defines how AI tools communicate with each other, share context, and request actions across a workflow.
In practice, MCP allows AI tools to act as servers that other AI tools can connect to and call directly. Stitch exposes an MCP server, which means a developer working inside an AI coding environment can call Stitch mid-workflow to generate a UI component without ever opening a browser. The request goes in, the generated UI and its corresponding DESIGN.md come back, and the workflow continues.
This is what it looks like when AI tools stop being isolated products and start becoming a connected workflow. Stitch with MCP is not just a faster design tool. It is a design service that other AI agents can consume programmatically.
Stitch vs. the alternatives
Stitch enters a space with strong, established players. Understanding where it fits helps us decide when to reach for it.
| Tool | How it compares to Stitch |
|---|---|
| Figma | Not a replacement. Stitch is the design starting point, Figma is where designs are refined and annotated |
| v0 by Vercel | Stitch is design-first (visual output plus code), v0 is code-first (component code plus preview) |
| Lovable | Stitch produces UI only, Lovable generates a full working application including backend logic |
| Galileo AI | A similar AI design tool that existed before Stitch, now largely superseded in the space |
The most practical way to think about these tools in 2026 is as complementary rather than competing. We can use Stitch to generate the initial UI, export DESIGN.md for our coding agent, use MCP to wire it into the development pipeline, and bring in Lovable or v0 for the remaining implementation work.
Limitations worth knowing
Stitch is a significant step forward, but approaching it with clear expectations matters.
- It does not support real-time collaboration yet. Teams cannot co-edit a Stitch session the way they can in Figma, which limits its use in larger organizations where simultaneous design work is a workflow requirement.
- Very custom or unconventional designs can challenge it. If a brand has highly specific visual language, such as unusual grid systems, non-standard component patterns, or heavily illustrated interfaces, Stitch will tend toward more generic output. It performs best with established UI conventions.
- Stitch carries the uncertainty that comes with any Google Labs product. Google’s history with experimental tools includes several high-profile discontinuations. That said, the presence of MCP integration and a structured DESIGN.md export format suggests Google is positioning Stitch as infrastructure rather than a short-lived experiment. Even so, it is worth treating Stitch as a generation tool, a place to create starting points, rather than a long-term design repository.
Who should use Stitch right now?
Stitch is well-suited to four groups in particular.
- Frontend developers will find it most immediately valuable for eliminating the blank-canvas problem. Instead of starting a new feature from scratch and spending hours on layout and styling decisions, we can generate a working starting point in minutes and build from there.
- UI/UX designers can use it to compress the early ideation phase. Rather than spending the first day of a project building low-fidelity wireframes, we can use Stitch to generate several UI directions quickly, select the most promising one, and take it into Figma for refinement. The time saved in the ideation stage alone justifies learning the tool.
- AI workflow builders and developers working with coding agents will find the MCP integration and DESIGN.md export particularly valuable. Stitch slots directly into automated pipelines, making it possible to generate UI components programmatically as part of a larger AI-assisted development workflow.
- Beginners and no-code builders now have a genuine entry point into professional-quality UI. Stitch removes the skill barrier that has historically kept non-designers away from building polished interfaces, making it one of the most accessible generative AI tools for newcomers to the field.
Stitch is free and accessible from any browser today. The best way to form a genuine opinion on a tool this new is to use it, and the three-prompt walkthrough from this blog is a ready-made starting point. We encourage you to open stitch, run the prompts, and see what comes back.