One thing I’ve been struggling with is detecting when LLM outputs are subtly wrong. Not obvious failures, just slightly incorrect or misleading answers that still look fine at a glance. Right now most of our checks are manual or based on user feedback, which doesn’t scale well. I’ve been looking into evaluation-based approaches and saw platforms like Confident AI that try to score outputs on things like faithfulness and relevance. Not sure how reliable these metrics are in practice though. Would be interesting to hear how others are handling this especially at scale. submitted by /u/Far_Revolution_4562
Originally posted by u/Far_Revolution_4562 on r/ArtificialInteligence
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