Building complex, multi-step AI processes directly with code generators creates a black box that is difficult to debug. Instead, prototype and validate the workflow step-by-step using a visual tool like N8N first. This isolates failure points and makes the entire system more manageable.

To enable AI agents to effectively modify your front-end, you must first remove global CSS files. These create hidden dependencies that make simple changes risky. Adopting a utility-first framework like Tailwind CSS allows for localized, component-level styling, making it vastly easier for AI to understand context and implement changes safely.

Despite sophisticated AI debugging tools that monitor logs and browsers, the most efficient solution is often the simplest. Highlighting an error message, copying it, and pasting it directly into an AI agent's chat window is a fast and reliable way to get a fix without over-engineering your workflow.

To accelerate design skill, seek out blunt feedback from practitioners you respect. Go beyond high-level user feedback and ask for a "roast" on the visual details. The goal is to get concrete, actionable advice—even down to specific CSS classes—to refine your taste and execution.

Many AI tools expose the model's reasoning before generating an answer. Reading this internal monologue is a powerful debugging technique. It reveals how the AI is interpreting your instructions, allowing you to quickly identify misunderstandings and improve the clarity of your prompts for better results.

Cursor's visual editor allows designers to make minor adjustments to UI elements like padding and spacing directly, bypassing the need for constant AI prompting. This speeds up experimentation but doesn't replace dedicated design tools like Figma.

Not every identified error requires building a formal evaluation. Some issues, like a simple formatting error, can be fixed directly in the prompt or code without an accompanying eval. Reserve the effort of building robust evals for systemic, complex problems that you anticipate needing to iterate on over time.

AI code generation tools can fail to fix visual bugs like text clipping or improper spacing, even with direct prompts. These tools are powerful assistants for rapid development, but users must be prepared to dive into the generated code to manually fix issues the AI cannot resolve on its own.

When an agent fails, treat it like an intern. Scrutinize its log of actions to find the specific step where it went wrong (e.g., used the wrong link), then provide a targeted correction. This is far more effective than giving a generic, frustrated re-prompt.