AI Data Labeling Template for AI Products

It depends on the task complexity and the number of categories. For text classification with 5-10 categories, 1,000-5,000 labeled examples per category is a reasonable starting point. For fine-tuning LLMs, even 100-500 high-quality examples can yield meaningful improvements with techniques like LoRA.

In-house labeling produces higher quality for domain-specific tasks but is expensive and slow. Vendors are faster and cheaper but require thorough guidelines and heavy QC. Most teams use a hybrid: in-house experts create guidelines and gold standards, vendors handle production volume.

A Cohen's kappa of 0.80 or higher is generally considered "substantial agreement" and is sufficient for most ML tasks. For high-stakes applications (medical, legal, financial), aim for 0.85+. Below 0.70, your guidelines likely need revision.

Disagreements are data, not failures. Track disagreement rates by label category to identify where your taxonomy is ambiguous. Have a lead annotator make the final call, and add the disputed example to your edge cases documentation.

Not yet for most production use cases. LLM-generated labels work well for pre-annotation (reducing human effort by 30-50%) and for prototyping when you need quick-and-dirty training data. But for production models, human-verified labels still produce more reliable training data, especially for domain-specific or nuanced categories.