Dataset curation is the process of selecting, cleaning, labeling, organizing, and maintaining examples for evaluation. It includes removing duplicates, balancing coverage, adding hard cases, preserving metadata, and deciding which production traces should become regression tests.
Curation is not busywork. The dataset defines what the system learns from and what the eval suite can see. A small curated dataset of real failures can be more useful than a large synthetic dataset with shallow coverage.