◇ Could this help me?
Yes. This is the correct instinct and the correct method: a dataset of real questions with known good answers, mixed scoring rather than one metric, and reading traces instead of only the headline score is what catches the plausible-sounding wrong answer. One caution the video skips: a model acting as judge has its own biases and needs to be spot-checked against human judgment on a sample, or you end up trusting a score that measures agreement rather than correctness. The specific evaluation product is one option among several, and the method matters more than the tool. This is a general practice.
Set up evaluations for the AI feature in this project. First help me build a dataset of real user questions paired with ideal answers, including the hard and ambiguous cases rather than only the easy ones. Then design a mixed scoring approach: deterministic checks for factual answers and a model-as-judge rubric for open-ended ones, and tell me how I should spot-check that judge against my own judgment. Finally, set it up so I can run the same dataset across different models and compare correctness and tone side by side.