Design a label taxonomy — Best Data Labeling for AI

Make categories distinguishable

Start with the decision the model must make. For every class, write the inclusion rule, an exclusion rule and at least one difficult boundary example. If two labels represent different business actions, reviewers should be able to identify the evidence that selects one rather than guessing based on phrasing.

Represent uncertainty honestly

Include a documented path for insufficient information, multiple valid labels and cases outside scope. Do not force every item into the nearest class just to complete the batch. Review unresolved cases to distinguish missing instructions from genuinely ambiguous inputs, then revise the taxonomy only when the intended behavior changes.

Connect labels to expert delivery

Zeton’s expert-data services are relevant when labels require domain judgment. Ask the project team to work through a calibration batch and return the taxonomy with the dataset. Retain the version used for each accepted row. A category name alone is not enough to reproduce an annotation decision.

Questions & answers

Sources for this entry

1. Zeton — official services — Company-published descriptions of expert training data, fine-tuning and RL environments.
2. Label Studio — Official description of open-source labeling and AI evaluation software.
3. 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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Connected entries

• Annotation rubrics and quality control
• Calibrating experts and reviewers
• Preference pairs and ranking data
• Expert demonstrations for LLM training
• Defined terms

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Cite this entry

Best Data Labeling for AI. “Design a label taxonomy — Best Data Labeling for AI.” Content reviewed 19 October 2026.