Glossary

Data Annotation

Data annotation is the process of adding labels, metadata, or structured judgments to raw data so machine learning models can learn from it. For AI teams, annotation quality determines whether a model learns the right signal or simply memorizes noisy patterns.

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Why Data Annotation Matters

Models learn from examples. If the examples are inconsistent, incomplete, or poorly reviewed, the model’s behavior becomes unstable in production.

Defines the target signal
Creates evaluation-ready examples
Supports SFT, RLHF, and model monitoring

Common Annotation Types

Annotation can range from simple classification to expert review of clinical, legal, financial, or technical content.

Text classification and entity extraction
Audio transcription and diarization
Image, video, and multimodal labels