Effectively tracking user feedback is essential for improving large language model (LLM) applications, as it helps refine AI responses and enhance user satisfaction. By implementing a continuous feedback loop through stages such as user interaction, feedback collection, pattern analysis, dataset creation, and prompt optimization, developers can systematically improve AI products and reduce operational costs. Tools like Helicone facilitate this process by offering methods to gather and analyze user feedback, including structured binary feedback via a Feedback API, custom properties for nuanced data, and advanced user metrics tracking for deeper insights. These strategies have shown to increase positive user interactions, as indicated by studies like those from Google DeepMind, and have led to significant operational efficiencies for companies such as Gorgias and Greptile. By turning collected feedback into specialized training datasets, developers can identify actionable insights and optimize their models accordingly, ultimately leading to improved application performance and user experience.