Tired of AI-ish UI? Here is how to make it better...

Introduction & The Problem with Default AI UIs 00:00

• Default AI-generated UIs often look generic and are easily identifiable as AI creations.
• The goal is to create more personalized and branded designs with unique interaction patterns.
• Once a high-quality UI component is established, it can be scaled to other components to maintain design consistency.
• The video presents a workflow to improve AI-generated UI output quality.

Flow Engineering vs. Prompt Engineering 00:27

• Introduces the concept of “flow engineering,” which involves breaking down expert design processes into sequential steps, rather than relying on a single prompt.
• The recommended workflow: layout decision, scene/branding development, high-fidelity mockup including animations, then front-end implementation.
• Iterative feedback at each step is key to refining the model's output.
• Other elements like reference modes, user flow, and content hierarchy can also be experimented with.

Step 1: Layout Alignment 01:39

• Typical AI-generated UIs are functional but often poorly detailed (e.g., misaligned elements or missing important info).
• Early layout alignment is important; using ASCII wireframes enables quick feedback loops with the AI model.
• Prompting the model to generate ASCII wireframes allows fast visualization and easy iteration.
• Examples include chat UIs and creative poster layouts, with flexible interaction demonstration (e.g., showing a sidebar appearing on menu click).
• Limitations: ASCII tends to oversimplify content hierarchy, but it is efficient for structuring layouts before CSS/HTML implementation.

Step 2: Scene/Style Customization 04:09

• “Scene” includes elements like color, font, shadow, and border-radius, which greatly influence personalization and branding.
• Style references can be sourced from platforms like Dribbble and imported into tools to extract CSS stylesheets.
• Platforms like Twix CN offer UI style customization, with options to modify design aspects and export code.
• Finalizing the style dramatically increases perceived quality; changing taste-driven aspects like fonts and colors can significantly differentiate UIs.
• Tools like Super Design streamline style generation by previewing multiple stylesheets and iterating on them as needed.

Step 3: Animation and Micro-Interactions 05:50

• Good UI is enhanced not just by visuals, but by thoughtful animations and micro-interactions (e.g., inline editing, hover effects, sliding elements).
• Prompting AI to think through and specify key animations (element to animate, keyframes, triggers) improves the quality of interactions in generated UIs.
• Advanced: Model can generate full user flows in mermaid chart format for more complex interaction designs, though simple keyframe prompts are sufficient for most projects.

Scaling and Building Consistent Component Libraries 07:01

• Once a satisfactory component is created, the workflow can be repeated to generate other components (like calendar views, map views, and property cards), ensuring style and interaction consistency.
• The model uses previous component context to maintain cohesion across new designs.
• This iterative approach allows building comprehensive, cohesive UI libraries efficiently.

Tools & Community Resources 07:58

• The Super Design extension (open-source, works inside the IDE) embodies this workflow, allowing layout, style, and animation iterations, and infinite canvas previews.
• The presenter has shared detailed workflows and prompt templates in the AI Builder Club community, which focuses on building and launching AI products.
• Resources include in-depth instructions on styling, icon scripts, and workflows for superior UI generation with AI tools.
• Viewers are encouraged to share their own tips and join the community for further exploration and collaboration.