- In short
- Discernment is the discipline of judging whether the model output is actually good, rather than accepting it because the metric dashboard looks healthy. Applied to production monitoring, it means periodically classifying outputs as acceptable, needs revision, or needs override, and feeding that judgment back into the evals. A team that only watches metrics move, without judging underlying output quality, can miss quality regressions the metrics do not capture, and discernment feedback should route back into the eval suite so future automated checks reflect what human judgment found.
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