Preetika Bhateja and Daniel Bump recommend beginning with a focused tool foundation and a small set of core tasks, then learning failure patterns before scaling evaluation. Their YouTube Ads examples show why teams need clear rating rubrics, shared examples and explanations rather than only pass-fail labels.
The presenters discuss calibrating automated judges against human ratings, inspecting traces and testing negative cases. An example in which an agent removes an explicitly protected disclaimer illustrates how an aggregate score can hide a serious failure. They advise measuring recurring patterns, refreshing tests with production evidence and defining acceptable tradeoffs before launch.
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