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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 but requires continuous effort throughout an AI product's lifecycle. As teams identify and fix errors through evals and improvements, new priorities and error modes emerge that require additional human labeling. When teams create new evals, they need corresponding human labels to validate the automated evaluation's accuracy.

This ongoing process maintains alignment between automated evals and human judgment. Teams must regularly verify that their automated evaluations continue to match human assessment as the AI system evolves and encounters new types of inputs.

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. After labeling a set of traces, teams analyze patterns to identify the most common failure modes.

These human-labeled examples serve multiple purposes: they validate that automated evals correctly identify good and bad outputs, they provide examples for few-shot prompting to improve the AI system, and they help teams understand which quality dimensions matter most for their specific use case.

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