Labels are product decisions
Data annotation is not clerical work. Every label defines what the AI system should learn, measure, or avoid.
Start with the goal
Decide whether the dataset is for classification, extraction, ranking, safety review, fine-tuning, retrieval evaluation, or output grading.
Write an annotation guide
The guide should define each label, show positive and negative examples, explain edge cases, and tell reviewers what to do when the answer is unclear.
Add quality control
Use review samples, agreement checks, ambiguous-case logs, and spot audits. The goal is consistency, not just volume.
Deliver data with context
A useful dataset should include labels, source information, definitions, review notes, and export formats that match the downstream use case.