Many users blame AI tools for generic designs when the real issue is a poorly defined initial prompt. Using a preparatory GPT to outline user goals, needs, and flows ensures a strong starting point, preventing the costly and circular revisions that stem from a vague beginning.

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.

Before delegating a complex task, use a simple prompt to have a context-aware system generate a more detailed and effective prompt. This "prompt-for-a-prompt" workflow adds necessary detail and structure, significantly improving the agent's success rate and saving rework.

When using "vibe-coding" tools, feed changes one at a time, such as typography, then a header image, then a specific feature. A single, long list of desired changes can confuse the AI and lead to poor results. This step-by-step process of iteration and refinement yields a better final product.

Users get frustrated when AI doesn't meet expectations. The correct mental model is to treat AI as a junior teammate requiring explicit instructions, defined tools, and context provided incrementally. This approach, which Claude Skills facilitate, prevents overwhelm and leads to better outcomes.

Open-ended prompts overwhelm new users who don't know what's possible. A better approach is to productize AI into specific features. Use familiar UI like sliders and dropdowns to gather user intent, which then constructs a complex prompt behind the scenes, making powerful AI accessible without requiring prompt engineering skills.

To get consistent, high-quality results from AI coding assistants, define reusable instructions in dedicated files (e.g., `prd.md`) within your repository. This "agent briefing" file can be referenced in prompts, ensuring all generated assets adhere to a predefined structure and style.

When an AI model makes the same undesirable output two or three times, treat it as a signal. Create a custom rule or prompt instruction that explicitly codifies the desired behavior. This trains the AI to avoid that specific mistake in the future, improving consistency over time.