PUTTING THE USER BACK INTO AI EVALUATION

Summary from the TLDR Design newsletter

Three researchers on Microsoft's AI team argue that while machine evaluations confirm a response meets set criteria, only users can judge whether it actually worked for them. Qualitative conversations with real users exposed gaps between what builders assumed was good and what users valued, including tone, context, and a fear of generic output. The team built a shared loss-pattern taxonomy and multi-turn evaluations that treat entire conversations, rather than single responses, as the unit of measurement.