For niche tasks, leverage an AI model with deep domain knowledge (like Claude for its own 'Skills' feature) to create highly specific prompts. Then, feed these optimized prompts into a powerful, generalist coding assistant (like Google's) to achieve a more accurate and robust final product.

Even though modern AI coding assistants can handle complex, single-shot requests, it's more reliable to build an application in stages. First, build the core functionality, then add secondary features, and finally add tertiary elements like download buttons. This iterative approach prevents the AI from getting confused.

For adding smaller, self-contained components like a chatbox or dark mode toggle, Atlassian created 'recipes.' These are pre-packaged code snippets with instructions that users can paste into an existing prototype. This modular approach avoids needing to start from a full template for minor additions, improving workflow flexibility.

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.

Developers can create sophisticated UI elements, like holographic stickers or bouncy page transitions, without writing code. AI assistants like CloudCode are well-trained on animation libraries and can translate descriptive prompts into polished, custom interactions, a capability many developers assume is beyond current AI.

Instead of asking an AI to directly build something, the more effective approach is to instruct it on *how* to solve the problem: gather references, identify best-in-class libraries, and create a framework before implementation. This means working one level of abstraction higher than the code itself.

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.

Designers have historically been limited by their reliance on engineers. AI-powered coding tools eliminate this bottleneck, enabling designers with strong taste to "vibe code" and build functional applications themselves. This creates a new, highly effective archetype of a design-led builder.

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.