When refining an LLM's output, relying on generic instructions like 'Please refine it!' provides no supervision on what exactly needs improvement and relies solely on the model's instruction-following ability. A more effective approach is to provide targeted feedback focusing on specific aspects of the output, such as prompting the model to 'correct all grammatical errors.' This specific guidance directs the model's attention during refinement, leading to more targeted and higher-quality improvements.