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 manually learning and implementing complex design techniques you find online, feed the URL of the article or example directly to an AI coding assistant. The AI can analyze the technique and apply it to your existing components, saving significant time.

To generate superior content ideas from a visual AI like Poppy, provide three types of inputs: links to viral videos for inspiration, links to your own content to define your style, and a link to an expert's analysis to provide strategic guidance.

Move beyond basic AI prototyping by exporting your design system into a machine-readable format like JSON. By feeding this into an AI agent, you can generate high-fidelity, on-brand components and code that engineers can use directly, dramatically accelerating the path from idea to implementation.

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.

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.

The true creative potential for AI in design isn't generating safe, average outputs based on training data. Instead, AI should act as a tool to help designers interpolate between different styles and push them into novel, underexplored aesthetic territories, fostering originality rather than conformity.

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.