A Practical Guide to Google Stitch (Formerly Galileo AI)

Design teams are under constant pressure to move faster, but the early stages of UI work still tend to slow everything down. That’s where Google Stitch AI—formerly Galileo AI—has started to change the pace. Many teams now use Stitch to handle the first 70–80% of UI creation, generating initial screens and layouts in minutes instead of days.

It doesn’t replace the thoughtful parts of design. Instead, Stitch by Google clears the way so your team can focus on the final 20%: UX decisions, flow improvements, and strategic choices that determine whether a product actually works for your users and supports your business goals.

At Gapsy Studio, we’ve been integrating AI into our design process, from early Galileo AI UI design experiments to today’s Stitch-powered workflows. We’ve seen where these tools genuinely add value and where human judgment is still irreplaceable. That experience is what inspired this practical guide to how Stitch fits into modern UI/UX and design-to-code workflows.

Google Stitch AI is Google’s latest step in bringing generative AI directly into real product design workflows. If you used Galileo AI design before, Stitch will feel familiar: it builds on the same prompt-to-prototype idea but is now deeply integrated into Google’s ecosystem.

Earlier in 2025, the news broke that Galileo AI tool was acquired by Google, and it has since been reimagined as Stitch. Under the hood, Stitch still uses the core engine that made Galileo UI design so popular, but it now runs on Gemini models, benefits from Google’s infrastructure, and connects more smoothly to tools like Figma and front-end code exports.

In practice, this means you can:

• Turn text prompts, sketches, or screenshots into multiscreen UI drafts.
• Generate responsive layouts for web and mobile design in one flow.
• Export structured HTML/CSS for faster collaboration with your development team.

For founders, product owners, and marketing leaders, Stitch isn’t just a shiny AI toy. It’s a way to remove the “blank canvas” phase from design so your team can get to decision-making faster.

With updates rolled out through late 2025, Stitch now offers smarter layout refinement, better responsiveness, and improved prototyping features tailored to modern UI/UX teams. In short, Stitch advances what Galileo started: turning early-stage design into a faster, more efficient, and far more automated process.

Before we dive into the mechanics of using it, we should probably address the elephant in the room: this isn't just a marketing pivot. If Galileo was a brilliant proof-of-concept, Google Stitch AI is the infrastructure built to support it.

The real shift here is about predictability. In the early days, Galileo felt like a creative partner that occasionally went off-script. Stitch, however, feels like a production-ready UI design tool. By grounding the original 'prompt-to-UI' idea in Google’s broader ecosystem, it has moved from being a space for “what-if” experiments to a reliable starting point for actual, shippable products.

Key differences at a glance:

If you ever hesitated to bring Galileo AI into your production workflow because of stability or handoff hurdles, Google Stitch is the reliable successor that finally turns your creative sparks into a professional-grade foundation.

At its core, Stitch by Google builds on Galileo’s foundation to deliver a faster, more automated way to create early-stage UI. Its real strength is simple: it translates your prompts, sketches, and screenshots into structured, responsive interface designs that your team can immediately refine.

Instead of replacing your design process, Stitch accelerates the slowest parts, so you spend less time drawing boxes and more time making product decisions.

Stitch focuses on three essential capabilities:

1. Text-to-UI Generation – Convert natural language prompts into ready-made interface layouts.
2. Image-to-UI Conversion – Turn sketches, screenshots, or legacy interfaces into editable prototypes.
3. Responsive, Code-Exportable Components – Generate UI that can be exported as structured code for faster developer handoff.

The magic happens when you stop seeing Stitch as a standalone shortcut and start seeing it as the connective tissue for your process. When combining it with a solid design system and professional UI/UX design services, you get designers and developers to speak the same language. Stitch can turn the typical “design-to-code” friction into a collaborative flow where the team stays aligned on the big-picture vision.

Text-to-UI: Turning Natural Language Into Interfaces

Stitch’s text-to-UI feature lets your team turn simple written descriptions into functional interface layouts. A designer, PM, or founder can write something like:

• “Create a mobile onboarding screen with a welcome message, two input fields, and a continue button.”
• “Generate a dashboard with a left sidebar, top navigation, KPI cards, and a table for recent transactions.”

Stitch interprets the prompt, applies common UI patterns, and instantly produces a structured layout that would normally take hours to draft.

For you, that means:

• Faster early ideation and alignment.
• Less time spent on low-level layout decisions.
• More capacity to focus on UX logic and business priorities.

The better your prompt, the more valuable the layout. Treat prompts like mini creative briefs for your Stitch AI workflows.

Image-to-UI: Turning Sketches Into Digital Prototypes

Not every idea starts as a sentence. Many of the best concepts still begin as whiteboard sketches, messy diagrams, or quick paper flows. Stitch helps you keep that speed while skipping manual redrawing.

You can upload:

• A photo of a whiteboard wireframe from a discovery workshop.
• A rough paper sketch outlining a signup flow.
• A screenshot of an outdated interface that needs modernization.

Stitch analyzes the structure, identifies components, and rebuilds the layout as a clean digital prototype, often in a format that can move straight into Figma for refinement.

This is especially useful when you’re working on:

• New website design directions that need quick stakeholder validation.
• Multi-step flows for mobile app design where speed matters more than polish at first.
• Legacy products that only exist as screenshots, not editable design files.

You keep the agility of a brainstorming session but skip the most expensive part: the hours spent manually turning rough concepts into high-fidelity UI/UX layouts.

Responsive, Code-Exportable Components

One of Stitch’s strongest advantages over older Galileo AI design flows is its ability to generate UI that is both responsive and exportable as structured code. Instead of static images, you get layouts that move you closer to real implementation.

For example, your team can:

• Export a landing page as clean HTML/CSS to speed up web development.
• Generate a multi-step form and hand it to developers with ready-to-use component code.
• Export a dashboard prototype with grid logic that adapts across breakpoints.

When plugging Stitch into a consistent design system and UI kit, you ensure that the first screen looks and feels exactly like the fiftieth. It creates a sort of “automatic guardrail” that keeps your UI/UX cohesive over time, even as the project scales or the team grows.

Integration With Figma and the Google Ecosystem

One of Stitch’s biggest strengths is how naturally it fits into tools your team already uses.

On the design side, you can export Stitch-generated layouts directly to Figma. The structure, hierarchy, and component logic remain intact, so your team refines spacing, states, and microinteractions instead of rebuilding screens from scratch. It feels like a “fast forward” button for your projects.

On the collaboration side, Stitch benefits from being part of the Google ecosystem. Because running on Gemini and living inside Google Labs, it ties naturally into Docs, Sheets, and Slides. That means:

• Product specs in Docs can sit next to Stitch-generated prototypes.
• Roadmap discussions in Slides can include interactive UI drafts.
• Comments and shared links work the way your team already expects.

This makes multi-team discussions smoother, especially when design, product, and engineering need to align quickly.

For organizations already inside Google’s ecosystem, Stitch feels less like a new tool and more like an acceleration layer for how you already collaborate.

As AI becomes part of everyday design work, the question isn’t “What can the tool do?” but “How does it help your team deliver better products faster?”

Here’s where Stitch consistently creates value in real projects.

Faster Design Cycles

Stitch removes the slowest stage of UI creation: the first usable draft. Instead of starting from a blank screen, you begin with a layout that already reflects common patterns.

For you, this can mean:

• Faster discovery and alignment with stakeholders.
• More room in the schedule for testing and refinement.
• Shorter time from idea to a testable prototype.

Teams using AI across multiple tools report 2–3x faster turnaround, and our experience reflects a similar acceleration, especially when we need to present several directions early in a project.

Reduced Repetitive Work

Much of UI design is repetition: cards, tables, forms, modals. Different projects, same patterns. Stitch handles them quickly and consistently. The Figma 2025 AI report shows developers at 82% satisfaction with AI tools, largely because they eliminate redundant steps; designers are more cautious but appreciate how AI reduces mechanical workload. 

This means technical and creative teams can focus on structure, messaging, and outcomes. If you spend hours recreating similar layouts across products, Stitch can reclaim that time.

Nonetheless, speed is a liability if it isn’t anchored to something real. To keep quality high, we recommend using AI’s velocity as a vehicle, but keeping human-centered design as the driver. It’s about ensuring that every generated screen is backed by a specific user insight or business logic. 

Improved Collaboration Across Teams

One thing we noticed early on is how much cleaner the design-to-development handoff becomes when the initial structure comes from Stitch. Because layouts are responsive and consistent from the start, engineers have fewer questions, and product managers understand screen logic sooner. The industry trend shows 68% of developers say AI improves work quality, which aligns with what our teams mention: fewer inconsistencies and faster implementation.

Easier Early Validation of Ideas

Previously, early validation required someone to sit down and create at least a mid-fidelity mockup. Now, a PM or founder can sketch an idea, upload it to Stitch, and have something testable within the same day.

That changes how you run discovery workshops, early product pitches, and internal concept reviews. Ideas don’t sit in notebooks; they get validated, challenged, or proven within hours instead of weeks.

More Room for Creative Refinement

The real value for designers is the space Stitch creates for deeper thinking. When AI handles scaffolding, designers have more bandwidth for:

• Flow logic and user journeys.
• Visual storytelling and hierarchy.
• Accessibility and inclusive UX.
• Microinteractions that make the product feel alive.

Our team has seen quality take a massive leap here, but we’ve also learned to watch out for “hyperfragility.” It’s a phenomenon where a small AI glitch cascades through a design, potentially undermining the whole structure. 

It’s a vivid reminder of a truth we live by: AI is brilliant at the start, but it’s the human eye that guarantees the finish.

When you compare AI design tools, the main question isn’t “Who has the most features?” It’s “Which tool fits your workflow and business goals best?”

Here’s how Google Stitch AI stacks up.

Stitch vs. Typical AI UI Generators

Many AI UI tools (e.g., Uizard, Visily, or Framer AI) are good at visuals. They generate screens, suggest colors, and apply spacing, but stop at the mockup. Stitch differentiates itself by focusing on:

• Responsive layouts.
• Exportable HTML/CSS.
• Stronger design-to-code workflows.

If your goal is to bridge design and development, not just generate nice-looking screens, this matters more than one-off visual tricks.

Stitch vs. UX Research-Centric Tools

UX research platforms or analytics-first solutions, like Maze, UserTesting, or Hotjar, focus on behavior insights, usability testing, and heatmaps. They’re powerful, but they come in after you have a prototype.

Stitch lives earlier in the process:

• It helps you generate testable prototypes faster.
• It gives your research team more variations to compare.
• It feeds into a continuous loop of design → test → refine.

Stitch vs. Legacy Design Workflows

It’s also useful to compare Stitch not just to other AI tools, but to traditional design methods. In a typical workflow: sketch → wireframe → mockup → prototype → dev hand-off, each step takes time and invites handoff noise. Stitch condenses several early stages—sketch to prototype—into a much shorter loop. For businesses, that means fewer design sprints, fewer dependencies, and faster time to decide.

Comparing Google Stitch and Other AI Design Tools in Detail

AI design tools may look similar on the surface, but their capabilities are quite different once you start using them in real workflows. 

Below is a simple, practical comparison that helps clarify where Stitch fits compared to UX Pilot and Figma’s native AI tools.

If your primary goal is to close the gap between a designer’s vision and a developer’s reality, Stitch has a clear edge. It’s built for that specific bridge. However, if your team is currently in the deep-dive phase of UX exploration, where you’re still questioning the core logic or just experimenting within the Figma canvas, Stitch works best as part of a broader ensemble.

Stitch removes real bottlenecks product teams face every week. These five scenarios show where it consistently delivers value across discovery, design, and collaboration.

Rapid Prototyping During Discovery Workshops

When teams are defining a new feature or product, speed matters more than polish. Stitch transforms early whiteboard sketches or paper notes into usable UI drafts within the same meeting.

Example: During a discovery call, a PM sketches a basic onboarding flow. The designer snaps a photo, uploads it to Stitch, and generates a clean draft ready for discussion.

How we’ve seen this at Gapsy: On projects involving early concept alignment—such as our initial workshops for Scoop Solar—rapid sketching played a major role in shaping direction. With Stitch, those early visuals could transition into workable prototypes even faster, helping teams reach clarity sooner.

Why it works: Faster alignment, immediate clarity, and less rework after workshops. Pairing this with early user insights from B2B user research helps teams validate direction before investing time. When teams are defining a new feature or product, speed matters more than polish. Stitch transforms early whiteboard sketches or paper notes into usable UI drafts within the same meeting.

Generating Multiple Layout Variations for Stakeholder Review

Designers frequently deal with meetings where a “simple” request for a sidebar alternative turns into a weekend of manual pixel-pushing. Stakeholders naturally want to explore different directions. Manually building these out is the quickest way to overwhelm a design team.

With Stitch, designers can quickly generate:

• Sidebar vs. top‑nav layouts
• Card‑based vs. table‑based dashboards
• Alternative content hierarchies

By using Stitch, you’re essentially moving from “describing” to “showing” in real-time. It allows the team to quickly generate distinct patterns, turning a vague debate into a productive comparison of real screens. It keeps the momentum high and ensures that when a decision is made, it’s based on something tangible. 

Accelerating Design Support for Fast-Moving Product Teams

Product teams may operate with tight sprint cycles, where missing a design deadline slows down developers, testing, and release schedules. Stitch helps generate the foundational screens and layouts in minutes, freeing creators to focus on flow logic, edge cases, and overall product quality instead of routine layout work.

Example: For a SaaS client, the team needs six variations of a settings page for A/B testing. Stitch quickly creates multiple layout versions with consistent component structure, while designers concentrate on interaction patterns, microcopy, and brand visuals. What normally took a full workday can be completed in just a few hours. 

Improving Designer ↔ Developer Collaboration

A major advantage of Stitch shows up at the handoff stage. Since it outputs responsive layouts and code-ready components, developers receive a more structured starting point, without guesswork or manual cleanup.

Example: While updating a complex dashboard, the dev team notices spacing differences and layout misalignments in the Figma file. Rebuilding the base in Stitch produces a responsive foundation with consistent hierarchy across breakpoints, eliminating inconsistencies upfront.

Impact:
• Smoother collaboration
• Fewer back-and-forth messages
• Faster, more predictable implementation

Recreating Legacy Interfaces for Modernization Projects

Redesigns often start with messy source material, like old screenshots, scattered screens, or no Figma handoff. Instead of recreating everything pixel-by-pixel, Stitch converts static images into editable UI components in minutes.

Example case: A client comes with screenshots of an outdated internal system. After uploading the images into Stitch, the team instantly receives editable layouts that could be reorganized, restyled, and updated to modern design standards.

Why it matters: The tool skips the tedious “recreate the interface manually” stage and moves teams straight into improvement mode.

Speed is addictive, but it can be a trap if it isn’t managed. In our work, we’ve found that the real wins don’t come from letting the AI run on autopilot; they come from treating Stitch as a high-velocity partner in the early stages of a project. It’s the difference between a fully automated engine and a high-performance vehicle: you still need a skilled driver to keep it on the road. 

Based on what we've learned in the trenches, here are the practices that consistently turn raw AI output into a professional-grade product

Write Structured, Specific Prompts

Stitch performs best when it understands intent clearly. Vague requests lead to generic layouts, while structured prompts produce layouts that require far less rework.

• Example of a weak prompt:  “Create a dashboard for users.” This leaves too many decisions to the model—layout, components, hierarchy, goals.
• Example of a strong prompt: “Create a desktop dashboard with a left sidebar (Home, Analytics, Billing), a top nav with user avatar, three KPI cards, and a table showing recent transactions with filters.”

The difference is immediate: Stitch arranges components intelligently, reflects expected hierarchy, and gives designers a draft with real structural value.

Our tip: Treat prompts like mini creative briefs. One or two sentences of context can save 30–40 minutes of manual rearranging.

Use Sketches for Faster Iteration

Stitch’s strength becomes even more apparent when paired with rough sketches. We often sketch flows during discovery calls or internal workshops—sometimes on paper, sometimes on a whiteboard—and upload them directly into Stitch.

Workflow example we’ve used:

1. Founder/Product Manager: sketches a quick outline of a new onboarding flow during a strategy session.
2. Designer: photographs the sketch → uploads to Stitch → receives a clean, editable UI in minutes.
3. Developer: reviews the generated layout early to flag technical considerations and ensure feasibility.
4. This is especially effective for multiple directions at once, like SaaS dashboards, booking systems, or B2B tools, where flows matter more than visuals early on. For complex products, we often supplement this with UX consulting to align product logic and user journeys.

Always Refine AI Outputs in Figma

Stitch creates an excellent starting point, but it’s not meant to replace the craft of design. We’ve consistently found that the best results come from exporting Stitch’s output into Figma, then refining:

• Spacing
• Hierarchy
• Brand styling
• Accessibility considerations
• Component consistency

Stitch is fast, but not perfect. Small AI misinterpretations, like spacing inconsistencies or overly literal interpretations of prompts, can snowball into bigger problems if not corrected. Figma is where the design becomes intentional.

Our rule of thumb: “Stitch drafts the structure. Figma turns it into a product.”

Validate Designs With Real Users

AI can accelerate creation, but it can’t validate whether the output is useful. We’ve seen situations where a layout generated by Stitch looked correct but didn’t match user expectations in testing.

For example, in our practice, Stitch generated a clean layout with KPI cards and charts for a financial dashboard concept. But during quick user validation, we learned customers expected certain metrics to appear above others and wanted filters more accessible. Two small insights fundamentally reshaped the layout.

Why validation matters:

• AI optimizes for patterns, not user goals.
• Stitch can suggest what’s “common,” but only users can confirm “what’s right.”
• This closes the loop between rapid generation and real usability.

Stitch accelerates early UI creation, but like any AI tool, it has boundaries. Understanding these limits helps teams use it strategically and avoid downstream issues.

AI Struggles With Brand Expression

Stitch understands structure well, but it doesn’t reliably capture the nuances of a brand, its personality, spacing conventions, motion patterns, or visual storytelling. Even when the layout is technically correct, the final expression needs human refinement to align with a design system or brand identity.

Accessibility Still Requires Human Review

Although Stitch follows common UI patterns, it doesn’t consistently enforce accessibility best practices such as contrast ratios, touch‑target sizing, and typographic hierarchy. Designers should always run accessibility checks, especially for products in regulated sectors like finance or healthcare.

Responsive Behavior Isn’t Always Perfect

Stitch can generate multi‑breakpoint layouts, but dense or highly structured interfaces still require manual tuning. Dashboards, data tables, and multi‑column forms may look strong on desktop but collapse awkwardly on smaller screens. Human oversight ensures consistency across devices.

Occasional AI “Hallucinations”

At times, Stitch introduces extra components, unnecessary states, or misaligned patterns that seem correct at first glance but create complexity during development. Reviewing spacing, nesting, and logic before handoff prevents issues later in the build process.

AI Can Lead to Over‑Standardized UX

If teams rely too heavily on AI suggestions, products can start to feel similar to one another—predictable patterns, familiar layouts, and less intentional decision‑making. Taking time to validate flows, reference user research, and shape unique interactions keeps your experience differentiated.

If Stitch has helped you handle the first 80% of your UI work, but you’re feeling the weight of that final, critical 20%, we’d love to help you bridge the gap. Whether you need a quick audit for UX clarity or a more structured hand-off process for your developers, our team at Gapsy is ready to step in. Drop us a line!

Stitch is still early in its evolution, but the direction is already visible. Google shapes it into a core layer of a broader ecosystem where AI supports design, engineering, and collaboration rather than living as an isolated tool.

Stitch as a Connected Piece of Google’s Product Ecosystem

Google is moving toward a workflow where AI blends into everyday product work. In this model:

• Gemini interprets UI patterns, code logic, and visual structure.
• Workspace acts as the unified space for discussions, comments, reviews, and documentation.
• Stitch turns all that intelligence into editable, responsive interfaces that teams can refine, prototype, and eventually ship.

In other words, Google wants AI to live inside existing workflows, not as one more separate design app you have to switch to.

What Improvements Teams Might See Next

Based on Google’s update cadence and the early Labs roadmap, several advancements seem likely:

• More context-aware generation. As Gemini improves, Stitch should better understand user flows, multi-step interactions, and UX logic instead of focusing solely on screen layout.
• Deeper integration between Workspace and Figma. Expect smoother movement between Docs, Slides, Meets, and prototypes — potentially even live updates during design reviews and calls.
• Stronger automation for code and components. Today Stitch exports HTML/CSS, but future versions may push into React/Vue component output, cleaner tokens, auto-variants, and component libraries aligned with team design systems.
• Adaptive design systems. Over time, Stitch may learn a team's visual rules (spacing, tone, color usage & interaction patterns) and apply them automatically rather than relying on manual corrections.

If this trajectory continues, Stitch could shift from being a “fast draft generator” into a collaborative engine that accelerates UI production while designers retain creative and UX control.

Stitch can give you a high-fidelity screen in seconds, but it can’t give that screen a soul. It’s a powerful starting canvas, but it lacks the human intuition that drives a product. It doesn't feel the “why” behind a user’s motivation, it can't sense the emotional triggers of a high-friction checkout, and it doesn't recognize the subtle brand signals that make a user trust one app over another. 

The designer’s value is in the professional "spidey-sense" that catches what an algorithm misses. When you're working within outsourced UI/UX design models, the strategic oversight is your insurance policy for long-term scalability.

A design team ensures:

• User flows are intuitive, not just visually arranged
• Branding feels deliberate and recognizable across screens
• Accessibility and UX best practices are applied consistently
• Every component supports real user tasks and business goals

AI speeds up creation. Designers elevate it into a coherent experience that users trust — and enjoy using.

How We Help

Gapsy Studio offers a way to navigate the "high-speed" reality of modern product development without losing the human intuition that makes a product successful.

We help teams: 

• Translate business logic into UX flows for complex products (dashboards, SaaS tools, fintech interfaces, multi-role systems).
• Design interaction patterns that scale as features grow instead of collapsing under new requirements.
• Handle edge cases and exceptions. 
• Strengthen usability and clarity in high-stakes contexts (finance, healthcare, logistics).
• Build and maintain design systems so AI-generated outputs stay aligned over time.
• Refine AI drafts into production-ready experiences that work across devices and scenarios.
• Make research and validation, grounding decisions in real user behavior.

If Stitch is entering your workflow and you need design experience that accounts for real product complexity, we’re here to help you shape AI output into something scalable and user-ready. Connect with us to discuss the next steps.

However, we have to remember that while AI can generate a structure, it can’t feel the context, the emotion, or the deep intention behind a product. The future means letting the machine handle the groundwork so that designers can focus their energy on insight, clarity, and innovation. Stitch provides the canvas, but great design still depends on the person holding the brush.

If you’re ready to see how AI-driven speed can work in harmony with high-level UI/UX design, we’re here to help you bridge that gap. Gapsy Studio is here to help you move past the "generic" and build something that resonates with your users and supports your business goals. Reach out and let’s discuss how we can bring your project to life with both speed and soul.