Annotation

Annotation is how raw data — a conversation, a piece of text, a model response — becomes useful training signal. An annotator might be asked to rate a response on helpfulness, identify whether it contains a hallucination, choose the better of two options, or flag a safety concern. Good annotation requires clear guidelines, well-calibrated annotators, and ongoing quality checks. For behavior architects, annotation sits at the intersection of your behavioral goals and your data: how you define the task for annotators directly determines what the model learns. Poorly designed annotation tasks often produce data that’s technically complete but behaviorally misleading.