Human Grader
What is a human grader?
A human grader is a person with domain expertise who manually reviews and evaluates AI outputs to determine whether they meet quality standards. Human graders annotate traces, identify errors and failure modes, and create the ground truth labels used to evaluate the accuracy of automated evals—both code-based and LLM-as-judge.
The role often starts with the product creator and can expand to include other subject matter experts, such as instructors or team members with relevant expertise.
What is the human grading process?
The human grading process involves manually reviewing traces, annotating problems by identifying what's wrong with specific responses, and recognizing patterns across multiple examples. Graders typically work through an interface—such as Airtable or a custom annotation tool—where they can systematically review outputs and mark them as reviewed.
These annotations serve dual purposes: they help identify common failure categories that become the basis for automated evals, and they create the ground truth data needed to validate that those automated evals work correctly.
How do human graders validate automated evals?
Human graders provide the reference standard against which automated judges are measured. By comparing automated eval outputs to human grader evaluations using metrics like true positives, false positives, and false negatives, teams can determine whether their automated evals accurately identify good and bad outputs.
Interestingly, this comparison can work both ways. Sometimes automated evals reveal problems in human grading rubrics, showing that the code-based eval actually performs better than initial human evaluation. This helps teams refine both their automated and manual evaluation processes.
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