When encountering a bug, copy and paste the error message directly into the LLM. Often, this is enough for the AI to identify and fix the problem without needing further explanation.

Monitor the LLM's responses closely; if it keeps generating incorrect code, reassess the context you're providing to improve its output.

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 AI agent acts like a development partner, asking questions and seeking clarification, which enhances the coding experience.

When using AI for coding, there is often a gap between the code generated by AI and the user's ability to modify it effectively, highlighting the need for better tools.

The AI can serve as a teacher, helping less experienced developers understand code implementations by explaining them line by line, which is more effective than traditional resources like Stack Overflow.

AI coding tools are transforming how developers seek help, moving from platforms like Stack Overflow to more advanced AI models that provide direct coding assistance.

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.

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