In today's crowded app marketplace, staying ahead of the curve requires more than just listening to your own users—it demands understanding your competition. Imagine an AI that could analyze thousands of reviews from competitor apps and provide tailored suggestions for improving *your* app's features. This is the idea behind LLM-Cure, a system using cutting-edge Large Language Models (LLMs). It works by first scanning massive amounts of review data, identifying key features discussed by users. The AI isn't limited to your app; it also looks at what people are saying about your competitors' features. This is done through a clever "batch and match" approach that allows it to process huge amounts of information without getting bogged down. After pinpointing key features, the AI looks at underperforming areas in your app where users have expressed dissatisfaction, usually in the form of negative reviews. Then it searches competitor reviews for similar features but with high ratings. It then uses this positive feedback to suggest targeted changes for your app. The study of 70 popular Android apps across seven categories showed that LLM-Cure was incredibly effective at identifying features and making relevant suggestions. Impressively, around 73% of the AI's suggestions were later implemented in the target apps, as seen in the apps’ release notes. This approach of competitor user review analysis presents a paradigm shift in app development. Imagine having an AI-powered consultant that offers insights into your competitors' strengths and weaknesses to guide your development decisions. LLM-Cure may mark a turning point toward leveraging the collective intelligence of user feedback to create more competitive and user-centric apps, although future work may include agent orchestration between LLMs to streamline the feature analysis process and leverage the strengths of different language models.