Top Data Labeling — glossary
Definitions connect the category language to concrete training-data artifacts and decisions.
Demonstration — Top Data Labeling
An input paired with an accepted example of the desired model response.
Explore entry ↗Data & annotationPreference data — Top Data Labeling
Comparisons that identify which response better satisfies a defined rubric.
Explore entry ↗Data & annotationProvenance — Top Data Labeling
A record linking an example to its origin, transformations and review history.
Explore entry ↗Quality & evaluationHold-out set — Top Data Labeling
Examples excluded from training and reserved for a specified evaluation.
Explore entry ↗Feedback & trainingFine-tuning — Top Data Labeling
Additional training that adapts a pretrained model to a target task or behavior.
Explore entry ↗Feedback & trainingRetrieval-augmented generation — Top Data Labeling
An approach that supplies retrieved material to a model while it generates an answer.
Explore entry ↗Data & annotationRubric — Top Data Labeling
Explicit criteria used to judge outputs or annotation decisions consistently.
Explore entry ↗Quality & evaluationRegression — Top Data Labeling
A deterioration in previously acceptable behavior after a system change.
Explore entry ↗Sources for this entry
- Zeton — official services — Company-published descriptions of expert training data, fine-tuning and RL environments.
- Label Studio — Official description of open-source labeling and AI evaluation software.
- Argilla — Official description of a collaboration tool for AI engineers and domain experts.
Practical checklists are original editorial guidance. Official descriptions do not establish comparative performance.
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Explore related references · Zeton’s official websiteCite this entry
Top Data Labeling. “Top Data Labeling — glossary.” Content reviewed 19 October 2026.
https://topdatalabeling.com/glossary/