Glossary
Ground Truth
Verified, factually correct data used as the reference standard for evaluating AI agent accuracy and performance.
What is Ground Truth?
Ground truth provides the definitive correct answers against which agent outputs are measured. This includes expert-labeled datasets, verified transaction outcomes, validated diagnoses, or authoritative reference data. Quality ground truth is essential for meaningful performance evaluation, as agents can only be as accurate as the standard they're measured against.
Creating ground truth requires careful validation, often involving domain experts, multiple independent reviewers, or algorithmic verification. Challenges include the cost of expert labeling, ensuring ground truth covers representative scenarios including edge cases, and maintaining accuracy as domains evolve. For some tasks, true ground truth may not exist, requiring proxy metrics or human judgment as approximations.
Example
A medical diagnosis agent is evaluated against ground truth consisting of 10,000 cases where diagnoses were confirmed through lab tests, biopsies, or long-term patient outcomes. The agent's diagnostic accuracy of 89% is measured by comparing its assessments to these verified outcomes.
How Signet addresses this
Signet's Quality dimension requires validated ground truth datasets for accurate agent evaluation. Agents tested against high-quality, representative ground truth receive more reliable quality scores than those evaluated on synthetic or unvalidated data.
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