Prototyping a new product from scratch risks creating a generic, "AI slop" design. To avoid this, use "inspiration sourcing": find screenshots from other apps (e.g., on Mobbin) that have the design aesthetic you want, and feed them to the AI as a style reference for specific features.
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Instead of accepting default AI designs, proactively source superior design elements. Use pre-vetted Google Font combinations for typography and find specific MidJourney 'style reference' codes on social platforms like X to generate unique, high-quality images that match your desired aesthetic.
To ensure AI prototypes match your product's design system, don't just describe the style. Instead, start by prompting the tool to "recreate" a screenshot of your live app. Refine this initial output to create a high-fidelity "baseline" template for all future feature prototypes.
Instead of creating multiple static mockups, prompt the AI to build a widget directly into a prototype that allows clicking through different design styles. This provides a live, interactive way to evaluate options within the actual user interface.
To create a distinctive retro UI, Cursor's designer researched historical UI patterns and assets—a process he calls "UI archeology." This provided specific constraints to the AI, preventing it from generating generic designs and allowing him to "paint" a unique style over standard components.
To break out of a linear design path, use AI tools that can generate multiple, distinct design options from a single prompt or command. For example, Magic Patterns’ '/inspiration' command produces four variants, allowing for rapid brainstorming and side-by-side comparison of different approaches.
A practical AI workflow for product teams is to screenshot their current application and prompt an AI to clone it with modifications. This allows for rapid visualization of new features and UI changes, creating an efficient feedback loop for product development.
Leverage AI as an idea generator rather than a final execution tool. By prompting for multiple "vastly different" options—like hover effects—you can review a range of possibilities, select a promising direction, and then iterate, effectively using AI to explore your own taste.
To avoid generic, 'purple AI slop' UIs, create a custom design system for your AI tool. Use 'reverse prompting': feed an LLM like ChatGPT screenshots of a target app (e.g., Uber) and ask it to extrapolate the foundational design system (colors, typography). Use this output as a custom instruction.
AI coding tools generate functional but often generic designs. The key to creating a beautiful, personalized application is for the human to act as a creative director. This involves rejecting default outputs, finding specific aesthetic inspirations, and guiding the AI to implement a curated human vision.
When exploring UI solutions, use a tool like Magic Patterns and its "Inspiration Mode" to generate multiple, distinct design approaches from a single prompt. By asking the AI to "think expansively and make each option differentiated," product managers can quickly explore a wide solution space and avoid getting stuck on a single initial idea.
