What is data labeling?

Data labeling (also called data annotation) means adding clear, useful tags to raw data—images, text, audio, video, or documents so that machine learning models can learn from it. Labels tell a AI model what’s in a picture, what a sentence means, who is speaking in audio, or whether an example or review content should be treated as “spam” or “not spam.”

In supervised learning, these labeled examples become the model’s “ground truth.” Better labels usually lead to more accurate models.

With generative AI (large language models and multimodal systems), data labeling also includes rating model responses, improving prompts/answers, and checking reasoning steps. These human ratings and edits help fine-tune and evaluate modern AI systems.

Example

Scenario: You’re automating parts of an insurance claims workflow. Your dataset includes crash photos, PDF claim forms, and short call recordings

1. Images: Draw polygons on damaged parts (bumper, hood, headlight), set a severity label, and tag weather/lighting as attributes.
2. Documents: Mark key fields and spans (policy number, VIN, claim amount, dates), classify document type (invoice, estimate, ID), and link line-items to totals.
3. Audio: Transcribe short phone calls with timestamps, label speaker turns, tag intent (“report accident,” “status check”), and hide Personally Identifiable Information.

How human-in-the-loop works in this case: A lightweight model pre-labels easy cases; human reviewers confirm or fix and flag edge cases.

Why it helps: You can now train a damage-detection model, a document-extraction model, and an intent router—cutting claim processing time and improving accuracy.

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