Classification & Labeling Output Checklist

Use this when an AI is assigning categories, labels, sentiment, or tags to a batch of items. - Pull a random sample (not just the first few) and manually check the labels against your own judgment — systematic errors often only show up outside the first handful. - Check the edge cases specifically: ambiguous items that could reasonably fall into more than one category — that's where labeling schemes usually break down. - Are the category definitions actually being applied consistently, or does the same kind of item get labeled differently across the batch? - If confidence scores are provided, check whether low-confidence items are actually more often wrong — if not, the confidence score isn't trustworthy and shouldn't gate downstream decisions. - Is there a category for "doesn't fit any of these" / "unclear," and is it being used appropriately, or is everything forced into the closest existing label even when none really fit? - Does the distribution of labels across the batch look plausible given what you know about the data, or does something look systematically skewed? - If this feeds an automated decision (routing, filtering, moderation), what happens on a wrong label — is the cost of a false positive and a false negative both acceptable? Don't extrapolate from a handful of good-looking examples to "the whole batch is fine" — sample deliberately, including cases you'd expect to be hard.