Data labeling is annotating raw data with the ground truth models learn from or are judged against: categories, entities, rankings, quality grades. The frontier
Annotating raw data with the ground truth models learn from or are judged against: categories, entities, rankings, quality grades. The 2026 frontier is expert judgment, preference rankings and domain-specialist annotations that post-training and evaluation depend on.
Partly: LLMs now pre-label at scale with humans verifying, which flipped the economics. But label quality still bounds everything downstream, models trained or evaluated on sloppy labels inherit the sloppiness invisibly, so verification remains essential.