Human-in-the-loop evaluation sends the cases machines are least sure about to human reviewers. Their labels reveal where automated judges are wrong, calibrate those judges, and seed the golden dataset. It is how you get the scale of automated evaluation with the trust of human review.
Why it matters
Automated judges are fast but can be wrong. Human review on the uncertain cases keeps quality high and, crucially, tells you where the judges are failing.
How it works
Low-confidence or high-stakes cases are routed to reviewers, who label them. Those labels calibrate the judges, and the hardest cases get promoted into the golden dataset for permanent regression coverage.