Instead of relying on complex text prompts, use a curated mood board as a direct visual input. Generative models like Midjourney can interpret the aesthetic, color, and style from images more effectively than from descriptive words, acting as a powerful communication shortcut.

Atlassian improved AI accuracy by instructing it to first think in a familiar framework like Tailwind CSS, then providing a translation map to their proprietary design system components. This bridges the gap between the AI's training data and the company's unique UI language, reducing component hallucinations.

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.

A custom instruction defines your design system's principles (e.g., spacing, color), but it's most effective when paired with a pre-defined component library (e.g., buttons). The instruction tells the AI *how* to arrange things, while the library provides the consistent building blocks, yielding more coherent results.

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.

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.

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.

Create a custom AI agent trained on the philosophies and techniques of designers you admire. Feed it their articles, code examples, and tweets to build a "visual design auditor" that provides feedback and suggestions aligned with their collective style.