The prompt template is a handy takeaway. Naming the user, the job to be done, and the screens up front makes a big difference in output quality, and “change one thing per follow-up” is advice I wish I had followed earlier.
For beginners, I think Visily and Banani are the easiest entry points, while v0 and Bolt.new make more sense once you’re comfortable with code. Which one would you start with if you could only pick a single tool?
Glad the prompt template helped! If I could only pick one tool, I’d start with Banani AI or Visily if you’re not comfortable with code, because you get clean, editable screens quickly and can refine them in Figma. If you’re already a developer, v0 is the better starting point since you get real components right away. Either way, change one thing per follow-up prompt.
The biggest hurdle with most AI UI generators is that the code handoff usually results in a mess of hardcoded values rather than mapping to a proper token architecture. I started using a Figma plugin called DS Sync recently to bridge that gap, as its AI code generation actually outputs proper CSS variables instead of hallucinating raw hex codes. Do any of the tools you evaluated handle token mapping natively during that handoff step?
That’s a great point! Most tools I tested still need some manual cleanup when it comes to design tokens and consistent styling. Native token mapping during handoff would be a huge improvement, especially for projects that need long-term scalability. I’ll definitely look into DS Sync!
The workflow section is what stood out to me, especially the idea that every handoff should carry the work forward. Most tool roundups only compare first-screen quality, so judging by handoff and editability is a much more realistic way to evaluate these.
I’m planning to try the Stitch → Figma → Bolt.new chain on a small project. Have you hit any problems with Auto Layout or component structure surviving the Figma export, or does it mostly hold up?
Thanks! In my experience Auto Layout mostly survives the Stitch export, but it only works in Standard Mode and from text prompts, not image inputs. Component structure is the weaker part, so I usually spend a few minutes cleaning up layers and naming before moving on. Test it on one screen first, then decide how much you trust it for the rest.
I appreciate the “Watch Out For” notes on every tool. The point about prototype quality being mistaken for product readiness is one I’ve seen happen on real teams. A polished Figma Make or Lovable demo can make stakeholders think the hard engineering work is already done.
I’d also add that accessibility and responsive behavior deserve a checklist step in the workflow, not just a mention at the end. Do you run any automated checks on generated screens before they go further down the chain?
Completely agree, a polished demo can make everyone assume the hard part is finished. I don’t run automated checks at every step. I add a manual review after the Figma refinement stage, covering contrast, focus states, and responsive breakpoints. Making that a formal checklist step in the workflow is a good idea, and I’ll include it in a future update.
Mayur Upadhyay delivers tech-driven solutions in Web Development, e-commerce development, and mobile app development, helping businesses to grow worldwide.
The “judge every tool by handoff, not by the first screen” line is the part that stuck with me. Most comparisons rank tools by how good the first generation looks, but the real cost shows up when you try to move that work into Figma or code and half of it has to be rebuilt.
I also like the chaining idea, especially generating options in Stitch or Banani and then refining one in Figma before making it interactive. One thing I’d add is to test the export with a real project early, since Figma export behavior can differ depending on the mode or input type.
To answer your question, I lean toward chaining 2-3 tools rather than sticking with one, as long as each handoff holds up. Curious which handoff has been the weakest for you in practice?
Thanks! I agree that testing exports with a real project early is important. For me, the weakest handoff is usually from AI-generated designs to editable Figma files, where layouts and components sometimes need manual cleanup. Chaining tools works best when each one adds value without creating more rework.
The “What AI Still Doesn’t Do” section is the part I’d want every team to read before adopting any of these. A generated screen can look polished and still fail on accessibility, edge cases, or how spacing feels on a real phone, and those are exactly the things that get skipped when the first draft looks this good.
The prompt template is also useful. Naming the user, the job to be done and the screens up front is what separates a usable draft from a generic one, and changing one thing per follow-up keeps the iterations from drifting.
I’d be interested to hear how you review the output before it goes anywhere. Do you have a checklist for accessibility and responsiveness, or is it mostly manual spot-checking?
Thanks for highlighting this! I usually combine manual spot-checking with a few key checks for accessibility, mobile responsiveness, spacing, and edge cases. AI-generated screens are a great starting point, but testing real interactions on different screen sizes is essential before calling a design ready. 🙌
Full-stack developer and tech writer who enjoys making complex ideas simple. I write about web development, system design, and practical coding techniques to help developers improve more quickly.
This is a helpful way to frame the tools, because most roundups treat them as competitors when they really cover different stages. Seeing Stitch, Banani and Visily as the “get to screens” step, and Figma Make, v0 and Bolt.new as the “make it real” step, makes the choice much easier for someone starting.
The caveats are what make the list trustworthy. Notes like Bolt.new not converting a web project to React Native later, or Visily’s advanced features using credits faster, are the kind of detail you only learn by actually testing the tools.
Out of the nine, which one surprised you most once you used it on a real project? I’d guess it’s one of the less hyped ones.
Thanks for the thoughtful feedback! 🙌 Banani surprised me the most because it made the early UI exploration process much faster than I expected. I also realized that choosing the right tool for each stage matters more than finding one tool that does everything.
Useful list, and I like that you judge each tool by handoff rather than by how good the first screen looks. One thing I’d add to “What AI Still Doesn’t Do” is design consistency across screens. Tools like Uizard generate screens independently rather than as a connected flow, so a design system or shared component library still has to come from a human.
I’m curious whether you’ve found a good way to keep tokens consistent (colors, spacing, typography) when chaining tools. Do you define them in Figma first and carry them forward, or fix things up after each export?
Thanks, and good addition. Consistency across screens is the gap I'd put at the top of that section too. Most of these tools are great at one screen and weak at a system.
For tokens, I define them in Figma first and carry them forward. Colors, spacing scale, and type styles go in as variables before I generate anything, and I paste the actual values into the prompt or import them where the tool allows. Then I treat each export as a draft. I rebind the generated values to the variables instead of fixing them one by one, which is much faster than cleaning up after every tool.
The rule that helped most is to keep one source of truth and never let a generated file become it. Anything a tool invents (a slightly different blue, an off-scale 18px gap) gets mapped back to a token or thrown out.
Are you using Variables or a plugin like Tokens Studio for this?
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The prompt template is a handy takeaway. Naming the user, the job to be done, and the screens up front makes a big difference in output quality, and “change one thing per follow-up” is advice I wish I had followed earlier.
For beginners, I think Visily and Banani are the easiest entry points, while v0 and Bolt.new make more sense once you’re comfortable with code. Which one would you start with if you could only pick a single tool?
Glad the prompt template helped! If I could only pick one tool, I’d start with Banani AI or Visily if you’re not comfortable with code, because you get clean, editable screens quickly and can refine them in Figma. If you’re already a developer, v0 is the better starting point since you get real components right away. Either way, change one thing per follow-up prompt.
The biggest hurdle with most AI UI generators is that the code handoff usually results in a mess of hardcoded values rather than mapping to a proper token architecture. I started using a Figma plugin called DS Sync recently to bridge that gap, as its AI code generation actually outputs proper CSS variables instead of hallucinating raw hex codes. Do any of the tools you evaluated handle token mapping natively during that handoff step?
That’s a great point! Most tools I tested still need some manual cleanup when it comes to design tokens and consistent styling. Native token mapping during handoff would be a huge improvement, especially for projects that need long-term scalability. I’ll definitely look into DS Sync!
The workflow section is what stood out to me, especially the idea that every handoff should carry the work forward. Most tool roundups only compare first-screen quality, so judging by handoff and editability is a much more realistic way to evaluate these.
I’m planning to try the Stitch → Figma → Bolt.new chain on a small project. Have you hit any problems with Auto Layout or component structure surviving the Figma export, or does it mostly hold up?
Thanks! In my experience Auto Layout mostly survives the Stitch export, but it only works in Standard Mode and from text prompts, not image inputs. Component structure is the weaker part, so I usually spend a few minutes cleaning up layers and naming before moving on. Test it on one screen first, then decide how much you trust it for the rest.
I appreciate the “Watch Out For” notes on every tool. The point about prototype quality being mistaken for product readiness is one I’ve seen happen on real teams. A polished Figma Make or Lovable demo can make stakeholders think the hard engineering work is already done.
I’d also add that accessibility and responsive behavior deserve a checklist step in the workflow, not just a mention at the end. Do you run any automated checks on generated screens before they go further down the chain?
Completely agree, a polished demo can make everyone assume the hard part is finished. I don’t run automated checks at every step. I add a manual review after the Figma refinement stage, covering contrast, focus states, and responsive breakpoints. Making that a formal checklist step in the workflow is a good idea, and I’ll include it in a future update.
The “judge every tool by handoff, not by the first screen” line is the part that stuck with me. Most comparisons rank tools by how good the first generation looks, but the real cost shows up when you try to move that work into Figma or code and half of it has to be rebuilt.
I also like the chaining idea, especially generating options in Stitch or Banani and then refining one in Figma before making it interactive. One thing I’d add is to test the export with a real project early, since Figma export behavior can differ depending on the mode or input type.
To answer your question, I lean toward chaining 2-3 tools rather than sticking with one, as long as each handoff holds up. Curious which handoff has been the weakest for you in practice?
Thanks! I agree that testing exports with a real project early is important. For me, the weakest handoff is usually from AI-generated designs to editable Figma files, where layouts and components sometimes need manual cleanup. Chaining tools works best when each one adds value without creating more rework.
The “What AI Still Doesn’t Do” section is the part I’d want every team to read before adopting any of these. A generated screen can look polished and still fail on accessibility, edge cases, or how spacing feels on a real phone, and those are exactly the things that get skipped when the first draft looks this good.
The prompt template is also useful. Naming the user, the job to be done and the screens up front is what separates a usable draft from a generic one, and changing one thing per follow-up keeps the iterations from drifting.
I’d be interested to hear how you review the output before it goes anywhere. Do you have a checklist for accessibility and responsiveness, or is it mostly manual spot-checking?
Thanks for highlighting this! I usually combine manual spot-checking with a few key checks for accessibility, mobile responsiveness, spacing, and edge cases. AI-generated screens are a great starting point, but testing real interactions on different screen sizes is essential before calling a design ready. 🙌
This is a helpful way to frame the tools, because most roundups treat them as competitors when they really cover different stages. Seeing Stitch, Banani and Visily as the “get to screens” step, and Figma Make, v0 and Bolt.new as the “make it real” step, makes the choice much easier for someone starting.
The caveats are what make the list trustworthy. Notes like Bolt.new not converting a web project to React Native later, or Visily’s advanced features using credits faster, are the kind of detail you only learn by actually testing the tools.
Out of the nine, which one surprised you most once you used it on a real project? I’d guess it’s one of the less hyped ones.
Thanks for the thoughtful feedback! 🙌 Banani surprised me the most because it made the early UI exploration process much faster than I expected. I also realized that choosing the right tool for each stage matters more than finding one tool that does everything.
Useful list, and I like that you judge each tool by handoff rather than by how good the first screen looks. One thing I’d add to “What AI Still Doesn’t Do” is design consistency across screens. Tools like Uizard generate screens independently rather than as a connected flow, so a design system or shared component library still has to come from a human.
I’m curious whether you’ve found a good way to keep tokens consistent (colors, spacing, typography) when chaining tools. Do you define them in Figma first and carry them forward, or fix things up after each export?
Thanks, and good addition. Consistency across screens is the gap I'd put at the top of that section too. Most of these tools are great at one screen and weak at a system.
For tokens, I define them in Figma first and carry them forward. Colors, spacing scale, and type styles go in as variables before I generate anything, and I paste the actual values into the prompt or import them where the tool allows. Then I treat each export as a draft. I rebind the generated values to the variables instead of fixing them one by one, which is much faster than cleaning up after every tool.
The rule that helped most is to keep one source of truth and never let a generated file become it. Anything a tool invents (a slightly different blue, an off-scale 18px gap) gets mapped back to a token or thrown out.
Are you using Variables or a plugin like Tokens Studio for this?