When the AI development environment gets stuck, try pasting your code directly into the LLM's website to get a different perspective on the problem.

If you're unsure about a bug, try switching models. Different AI models may succeed where others fail, and if you find the source of a bug, reset all changes and give specific instructions to fix it on a clean codebase.

The challenge of debugging is that it requires context and experience, which AI lacks. While AI can help with trivial bugs, it struggles with complex logical issues.

AI can assist in debugging, but the real art of programming lies in finding complex bugs that require human intuition and experience.

Write clear instructions for the LLM, using the appropriate naming conventions for each tool. Founders have successfully written extensive instructions to guide their AI coding efforts.

To prevent AI from generating misleading information, it's important to give it an escape hatch, allowing it to indicate when it lacks sufficient information to make a decision.

The AI agent's edit tool functions similarly to a human coder, providing error feedback from a language server, which helps in identifying mistakes during coding.

The general guidance for prompting AI is to ensure that all necessary context is provided, especially when the AI is unfamiliar with the situation.

One innovative approach to providing AI with an escape hatch is to allow it to express confusion or request clarification when faced with ambiguous instructions, which can help improve the quality of its outputs.