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Plain-language AI glossary

Term 57TechniquesMeaning / context / connections

Techniques / Definition

Data Labeling

The process of annotating raw data (text, images, audio) with labels or tags so it can be used to train and evaluate machine learning models.

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MeaningThe one-sentence definition.

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01 / Plain-language context

How Data Labeling works.

Data labeling assigns useful targets to examples so a model can learn or an evaluation can measure performance. Labels might identify objects, sentiment, entities, categories, preferred responses, or whether an output meets a rubric.

Human annotators remain essential where context and judgment matter. Models can propose labels, active learning can prioritize uncertain examples, and synthetic data can expand coverage, but each approach needs quality controls. Clear guidelines, representative samples, disagreement analysis, class balance, and periodic audits matter more than the raw number of labels.

02 / Practical uses

Where it helps.

  1. 01Training custom classification models
  2. 02Creating evaluation benchmarks
  3. 03Fine-tuning LLMs on domain data
  4. 04Computer vision dataset creation
  5. 05Quality assurance for AI outputs
03 / Keep exploring
Fine-TuningThe process of further training a pre-trained AI model on your specific data to improve performance on domain-specific tasks.Synthetic DataArtificially generated data that mimics real-world data, used for training AI models when real data is scarce, expensive, private, or biased.Reinforcement Learning from Human Feedback (RLHF)A training technique that aligns AI model behavior with human preferences by using human feedback to reward desired outputs and penalize undesired ones.Computer VisionThe field of AI that enables machines to interpret and understand visual information from images and video.