AI Glossarytechniques

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.

How It Works

Data labeling is the foundation of supervised machine learning. Models learn patterns from labeled examples: "this image contains a cat" (image classification), "this sentence is positive" (sentiment analysis), "these words are a person's name" (NER). Without high-quality labels, models cannot learn effectively. Traditional labeling is done by human annotators, which is slow and expensive. Modern approaches use LLMs to accelerate labeling: (1) LLM-assisted labeling — GPT-5-mini labels data and humans review, reducing cost by 80%. (2) Active learning — the model identifies the most uncertain examples and asks humans to label only those. (3) Synthetic data generation — use an LLM to generate labeled training examples from scratch. Label quality directly determines model quality. Common issues: inconsistent labeling guidelines, annotator disagreement, label noise (wrong labels), and class imbalance (too many examples of one type). For production ML, invest heavily in clear annotation guidelines, inter-annotator agreement metrics, and quality auditing processes.

Common Use Cases

  • 1Training custom classification models
  • 2Creating evaluation benchmarks
  • 3Fine-tuning LLMs on domain data
  • 4Computer vision dataset creation
  • 5Quality assurance for AI outputs

Related Terms

Fine-Tuning

The process of further training a pre-trained AI model on your specific data to improve performance on domain-specific tasks.

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 Vision

The field of AI that enables machines to interpret and understand visual information from images and video.

Synthetic Data

Artificially generated data that mimics real-world data, used for training AI models when real data is scarce, expensive, private, or biased.

Need help implementing Data Labeling?

AI 4U Labs builds production AI apps in 2-4 weeks. We use Data Labeling in real products every day.

Let's Talk