Imagine having an AI tutor that instantly provides feedback on your code. That's the promise of large language models (LLMs) like GPT. But getting truly *helpful* feedback isn't as simple as just pasting your error message. A new research paper dives into the art of "prompt engineering" – crafting the perfect instructions to get the most insightful responses from AI. Researchers systematically tested different prompting methods on common R programming errors. Surprisingly, they found that simply telling the LLM to analyze the code step-by-step drastically improved the precision of its feedback. Even more counterintuitive, being *too* specific about what information to look at actually hindered the LLM's ability to identify the root cause of errors. This suggests that LLMs can sometimes get overwhelmed by too much detail, performing better when allowed to explore the code's context more freely. This study reveals a fascinating tension: while careful guidance improves the AI's focus, excessive instruction can limit its ability to understand the bigger picture. The research also offers a practical framework for evaluating different prompting strategies, paving the way for smarter, more effective AI code feedback in the future. As LLMs become integrated into coding education and professional development, understanding how to best communicate with them will be essential for unlocking their full potential as virtual coding assistants.