Before promoting a new build of a tutoring-support agent, a school-district IT team has staff review only the transcripts the pipeline flags as low-confidence, approving or correcting those before rollout rather than reading every transcript. What is the advantage of targeting review at low-confidence transcripts instead of reviewing the full transcript set?

Confidence-targeted human-in-the-loop review works because confidence scores correlate, imperfectly but usefully, with the likelihood of an error, so directing reviewer attention there yields a higher error-catch rate per hour of review than spreading the same reviewer time evenly across a mostly-correct transcript set. That's the operational payoff of the design, and it scales to production volumes a full-transcript review could never keep up with. Claiming it guarantees zero errors overstates what confidence scoring can do — a confidently wrong answer, one the model produced with high certainty despite being incorrect, would slip past this filter entirely. Claiming it removes the need for a golden dataset conflates two different evaluation mechanisms; automated golden-dataset evaluation and human low-confidence review serve complementary purposes and neither substitutes for the other. Claiming it eliminates the need for confidence scoring gets the dependency backwards — the entire review-targeting strategy depends on a working confidence signal existing in the first place. Scope caveat: the confidence threshold that defines low-confidence needs periodic tuning, since a threshold set too high lets genuine errors through unreviewed. Operational check: periodically sample a batch of high-confidence transcripts anyway and confirm the error rate there stays low enough to justify skipping full review.