Once a team stops trusting leaderboards, the next move is usually to build an evaluation of its own. We've argued before that benchmark scores are a poor predictor of production agent performance, and the natural follow-up question is what to measure instead.
Once a team stops trusting leaderboards, the next move is usually to build an evaluation of its own.
The first is a panel of LLM judges: ask several models whether an output is factually supported, and take the majority vote.
We've argued before that benchmark scores are a poor predictor of production agent performance, and the natural follow-up question is what to measure instead.
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Once a team stops trusting leaderboards, the next move is usually to build an evaluation of its own. We've argued before that benchmark scores are a poor predictor of production agent performance, and the natural follow-up question is what to measure instead. Two answers come up almost every time, because they are the two cheapest evals to stand up.
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