Comparison
Annotation vs Ground Truth
Annotation and Ground Truth are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Annotation
Annotation comes up when the question is fundamentally about evaluation.
A team samples 200 production traces weekly, routes them to an internal Argilla instance, and has reviewers label correctness + a category tag.
When you would reach for Ground Truth
Ground Truth comes up when the question is fundamentally about evaluation.
For a coding eval: ground truth = the passing tests in the repo at HEAD.
Frequently asked
What is the difference between Annotation and Ground Truth?
Annotation: Annotation is the process of attaching ground truth or quality labels to data — by humans, sometimes augmented by an LLM. The unglamorous but decisive lever in LLM evaluation. Ground Truth: Ground truth is the known-correct answer for an eval input. For supervised tasks it is the label used to grade model outputs; for LLM apps it is often human-curated reference answers.
When should I use Annotation vs Ground Truth?
Annotation is the right concept when you are focused on evaluation. Ground Truth applies when you are focused on evaluation.
Are Annotation and Ground Truth the same thing?
No. Annotation is evaluation; Ground Truth is evaluation. They are related but address different parts of the AI stack.