Dmitry Kuchin

This talk emphasizes a practical, user-centric approach to building reliable AI applications, moving beyond generic data science metrics. The core thesis is that AI app reliability stems from defining and testing against real-world scenarios and desired business outcomes, rather than abstract measures like factuality or groundness. This method allows for continuous improvement and confidence in the application's performance.