Optimizing LLM Apps Through Usage: Implicit Feedback, Given Explicitly

In the rapidly evolving landscape of LLM-powered applications, AI teams face a unique challenge: gathering actionable insights from real-world usage to continuously improve app performance. This presentation will explore the common obstacles teams encounter when attempting to optimize their applications based on user interactions. We will dive into how implicit feedback—often hidden within everyday user behavior—can be harnessed effectively to drive measurable improvements. By sharing our approach to extracting and leveraging this data, we'll demonstrate how it accelerates the development of smarter, more responsive LLM applications.

Chinar Movsisyan, CEO of Feedback Intelligence, shares insights on using implicit user feedback to optimize LLM applications. The talk covers identifying user intent, analyzing usage patterns, and leveraging this data to improve AI model performance and user satisfaction. Examples from fintech and customer support are discussed, along with tools for enhancing data sets and flagging queries. The presentation highlights the importance of moving beyond explicit feedback to unlock the full potential of LLM-based products.