Human Labeling

What is human labeling?

Human labeling is the process of manually reviewing and annotating AI outputs to create a labeled dataset that can be compared against automated evaluations. In AI product development, human labeling involves having people—often domain experts or product team members—examine AI-generated responses and mark them as correct or incorrect, categorize failure modes, or rate quality dimensions.

This labeled data establishes ground truth for evaluating and improving the AI system, serving as the reference standard against which automated evals are validated.

Why is human labeling ongoing work?

Human labeling isn't a one-time activity—it requires continuous effort throughout an AI product's lifecycle. As teams fix errors, new priorities and error modes emerge that require additional labeling, and every new eval needs corresponding human labels to validate its accuracy. This ongoing work keeps automated evals aligned with human judgment as the AI system evolves.

How does human labeling support AI evaluation?

Human labeling creates the foundation for multiple evaluation activities. Teams review traces—the AI inputs and outputs—and annotate them with notes about errors, quality assessments, and failure categories, then analyze patterns to identify the most common failure modes. These labeled examples validate that automated evals correctly identify good and bad outputs, provide examples for few-shot prompting, and help teams understand which quality dimensions matter most.

How can an LLM help with human labeling?

One trick is to do your human labeling with an LLM like Claude. Have it present one example at a time, apply your label—well framed or poorly framed—and explain why. The LLM watches for patterns and helps you turn what you do intuitively into explicit rules. After a few dozen examples, you'll have enumerated your implicit rules—rules you can give to an LLM judge or an agent prompt.

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Last Updated: September 16, 2026