This talk will walk through our current AI feature quality evaluation and improvement framework.

It will cover some of the existing challenges of evaluating the quality of LLM applications, and walk through our approach for constructing this framework to tackle some of these challenges. 

Then touches on common topics within the realm of quality evaluation, such as data collection, metric definition, and the quality improvement cycle, and continuously explores better ways to improve AI features' quality.