Imagine giving an AI coding instructions and it starts off strong, only to lose its way and produce buggy or irrelevant code. Frustrating, right? This "attention dilution" problem is a common issue in large language models (LLMs) used for code generation. They tend to get distracted by the code they've already written and forget the initial instructions as they go. Researchers have developed a clever technique called Selective Prompt Anchoring (SPA) to combat this. It works by essentially reminding the LLM of the most crucial parts of your instructions throughout the code generation process. Think of it as highlighting the key phrases in your prompt that the LLM absolutely shouldn't forget. Specifically, SPA analyzes the differences in the LLM’s output when the key parts of the prompt are present versus when they're masked out. This difference helps identify the contextual contribution of those key instructions. SPA then amplifies this contribution, ensuring the LLM stays focused on the original goal. Experiments show impressive results. SPA boosted the accuracy of generated code by up to 9.7% across various LLMs and coding tasks. Remarkably, a smaller LLM with SPA enabled actually outperformed a much larger model without it, suggesting that attention, not just size, is critical for code generation. SPA isn't just about boosting performance. It also has implications for controlling LLM behavior more generally. Imagine being able to fine-tune an LLM's focus during generation without any retraining. This opens exciting possibilities for more accurate and reliable code generation in the future. While SPA currently uses a fixed weighting parameter to control the anchoring effect, future research could explore dynamic adjustments based on the specific context and stage of code generation. This could lead to even smarter and more adaptable code generation AI tools.