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GenAI for UI/UX Mockups
Year
2024
Event
GAI Learning Lab
Focus
AI, Product Design, UI/UX, Text-to-UI, Design Systems, Figma
Overview
I started by demonstrating how Galileo AI could translate a natural-language product description into multiple mobile UI concepts for a space tourism booking experience. The generated screens included destination exploration, pricing, flight details, and booking flows. By adjusting the prompt, I was able to explore different layouts, visual styles, and interface directions without manually designing every variation. I then showed how the generated designs could be customized using different colors, typography, imagery, and visual themes.
The designs were exported into Figma as editable interface elements rather than flat images. This allowed individual components, layouts, and content to be refined using an established product design workflow.
I also shared a smart home prototype that began with AI-generated screens and was developed into a more complete interactive product concept. The goal was to demonstrate that AI does not replace the designer or create the final product. It changes the starting point by compressing brainstorming, reference gathering, rough wireframing, and first-pass visual exploration into a much faster process.
Talk Premise
I started by demonstrating how Galileo AI could translate a natural-language product description into multiple mobile UI concepts for a space tourism booking experience. The generated screens included destination exploration, pricing, flight details, and booking flows. By adjusting the prompt, I was able to explore different layouts, visual styles, and interface directions without manually designing every variation.
I then showed how the generated designs could be customized using different colors, typography, imagery, and visual themes. The designs were exported into Figma as editable interface elements rather than flat images. This allowed individual components, layouts, and content to be refined using an established product design workflow.
I also shared a smart home prototype that began with AI-generated screens and was developed into a more complete interactive product concept. The goal was to demonstrate that AI does not replace the designer or create the final product. It changes the starting point by compressing brainstorming, reference gathering, rough wireframing, and first-pass visual exploration into a much faster process.
Key Ideas
- Generative AI can turn natural-language prompts into structured UI concepts within minutes.
- Text-to-UI tools can accelerate brainstorming, early design exploration, and stakeholder alignment.
- Editable design output is more useful than static image generation because it can be moved into Figma and refined.
- These tools can help non-designers communicate product ideas more clearly through visual mockups.
- Designers can spend less time creating first-pass concepts and more time on interaction design, refinement, systems thinking, and product judgment.
- AI-generated interfaces still require human critique, product context, accessibility thinking, and visual craft.
- AI design tools will become more valuable when they understand company design systems, brand guidelines, component libraries, accessibility standards, and existing product patterns.
- Future tools may help generate more production-aligned interfaces, evaluate component usage, identify accessibility issues, and improve layout consistency.
- AI will supplement established product design platforms rather than replace tools such as Figma.
- The primary value of AI-assisted design is not producing the final interface. It is giving designers a stronger starting point to evaluate, refine, and improve.
© 2026 Sarat Kollimarla · Updated June 2026
.
Back to home
GenAI for UI/UX Mockups
Year
2024
Event
GAI Learning Lab
Focus
AI, Product Design, UI/UX, Text-to-UI, Design Systems, Figma
Overview
I started by demonstrating how Galileo AI could translate a natural-language product description into multiple mobile UI concepts for a space tourism booking experience. The generated screens included destination exploration, pricing, flight details, and booking flows. By adjusting the prompt, I was able to explore different layouts, visual styles, and interface directions without manually designing every variation. I then showed how the generated designs could be customized using different colors, typography, imagery, and visual themes.
The designs were exported into Figma as editable interface elements rather than flat images. This allowed individual components, layouts, and content to be refined using an established product design workflow.
I also shared a smart home prototype that began with AI-generated screens and was developed into a more complete interactive product concept. The goal was to demonstrate that AI does not replace the designer or create the final product. It changes the starting point by compressing brainstorming, reference gathering, rough wireframing, and first-pass visual exploration into a much faster process.
Talk Premise
I started by demonstrating how Galileo AI could translate a natural-language product description into multiple mobile UI concepts for a space tourism booking experience. The generated screens included destination exploration, pricing, flight details, and booking flows. By adjusting the prompt, I was able to explore different layouts, visual styles, and interface directions without manually designing every variation.
I then showed how the generated designs could be customized using different colors, typography, imagery, and visual themes. The designs were exported into Figma as editable interface elements rather than flat images. This allowed individual components, layouts, and content to be refined using an established product design workflow.
I also shared a smart home prototype that began with AI-generated screens and was developed into a more complete interactive product concept. The goal was to demonstrate that AI does not replace the designer or create the final product. It changes the starting point by compressing brainstorming, reference gathering, rough wireframing, and first-pass visual exploration into a much faster process.
Key Ideas
- Generative AI can turn natural-language prompts into structured UI concepts within minutes.
- Text-to-UI tools can accelerate brainstorming, early design exploration, and stakeholder alignment.
- Editable design output is more useful than static image generation because it can be moved into Figma and refined.
- These tools can help non-designers communicate product ideas more clearly through visual mockups.
- Designers can spend less time creating first-pass concepts and more time on interaction design, refinement, systems thinking, and product judgment.
- AI-generated interfaces still require human critique, product context, accessibility thinking, and visual craft.
- AI design tools will become more valuable when they understand company design systems, brand guidelines, component libraries, accessibility standards, and existing product patterns.
- Future tools may help generate more production-aligned interfaces, evaluate component usage, identify accessibility issues, and improve layout consistency.
- AI will supplement established product design platforms rather than replace tools such as Figma.
- The primary value of AI-assisted design is not producing the final interface. It is giving designers a stronger starting point to evaluate, refine, and improve.
© 2026 Sarat Kollimarla · Updated June 2026
.
Back to home
GenAI for UI/UX Mockups
Year
2024
Event
GAI Learning Lab
Focus
AI, Product Design, UI/UX, Text-to-UI, Design Systems, Figma
Overview
I started by demonstrating how Galileo AI could translate a natural-language product description into multiple mobile UI concepts for a space tourism booking experience. The generated screens included destination exploration, pricing, flight details, and booking flows. By adjusting the prompt, I was able to explore different layouts, visual styles, and interface directions without manually designing every variation. I then showed how the generated designs could be customized using different colors, typography, imagery, and visual themes.
The designs were exported into Figma as editable interface elements rather than flat images. This allowed individual components, layouts, and content to be refined using an established product design workflow.
I also shared a smart home prototype that began with AI-generated screens and was developed into a more complete interactive product concept. The goal was to demonstrate that AI does not replace the designer or create the final product. It changes the starting point by compressing brainstorming, reference gathering, rough wireframing, and first-pass visual exploration into a much faster process.
Talk Premise
I started by demonstrating how Galileo AI could translate a natural-language product description into multiple mobile UI concepts for a space tourism booking experience. The generated screens included destination exploration, pricing, flight details, and booking flows. By adjusting the prompt, I was able to explore different layouts, visual styles, and interface directions without manually designing every variation.
I then showed how the generated designs could be customized using different colors, typography, imagery, and visual themes. The designs were exported into Figma as editable interface elements rather than flat images. This allowed individual components, layouts, and content to be refined using an established product design workflow.
I also shared a smart home prototype that began with AI-generated screens and was developed into a more complete interactive product concept. The goal was to demonstrate that AI does not replace the designer or create the final product. It changes the starting point by compressing brainstorming, reference gathering, rough wireframing, and first-pass visual exploration into a much faster process.
Key Ideas
- Generative AI can turn natural-language prompts into structured UI concepts within minutes.
- Text-to-UI tools can accelerate brainstorming, early design exploration, and stakeholder alignment.
- Editable design output is more useful than static image generation because it can be moved into Figma and refined.
- These tools can help non-designers communicate product ideas more clearly through visual mockups.
- Designers can spend less time creating first-pass concepts and more time on interaction design, refinement, systems thinking, and product judgment.
- AI-generated interfaces still require human critique, product context, accessibility thinking, and visual craft.
- AI design tools will become more valuable when they understand company design systems, brand guidelines, component libraries, accessibility standards, and existing product patterns.
- Future tools may help generate more production-aligned interfaces, evaluate component usage, identify accessibility issues, and improve layout consistency.
- AI will supplement established product design platforms rather than replace tools such as Figma.
- The primary value of AI-assisted design is not producing the final interface. It is giving designers a stronger starting point to evaluate, refine, and improve.
© 2026 Sarat Kollimarla · Updated June 2026
.