Data labeling: Data labeling is the work of attaching correct answers to raw data, such as tagging emails as spam, drawing boxes around pallets in photos, or rating chatbot replies, so a machine learning model can learn from those examples.
Most useful machine learning is supervised, meaning the model learns from examples that already carry the right answer. Someone has to supply those answers. That is labeling: reading ten thousand support tickets and tagging each with a category, marking which transactions were fraud, transcribing audio, or ranking two AI responses to say which is better. The labeled set becomes the model's ground truth.
Quality matters more than volume. If two labelers would tag the same ticket differently, the model learns the disagreement. Good labeling projects start with written guidelines and edge-case examples, measure how often labelers agree, and route ambiguous items to an expert. Labels also go stale: a category that made sense last year may not match how customers write today.
Labeling is a real industry, with vendors and crowdsourcing platforms supplying workers, and it is where much of the human effort behind AI actually happens. The human feedback used to tune chat assistants is labeling too. And every time you click thumbs up or thumbs down on an AI reply at work, you are producing a label the vendor may use to improve its model, so check what your company's terms allow.
Example at work
A customer support manager wants an AI tool to route incoming tickets. Before anything is built, her team labels 1,500 past tickets with one of eight categories using a one-page guide. The first pass shows two categories overlap so often that agents cannot agree, so she merges them. The routing model trained on the cleaned labels is far more accurate than one trained on the original set.
Why it matters
If your team is asked to help train or evaluate an AI system, labeling is the job. Knowing that the quality of those labels sets the ceiling for the model helps you push for clear guidelines instead of rushing through it.
Related terms
- Supervised learningSupervised learning trains a machine learning model on examples that already carry the correct answer, so it can predict the answer for new cases. It is behind spam filters, credit scoring, demand forecasts, and most business AI that classifies or predicts.
- Training dataTraining data is the collection of examples an AI model learns from, such as web pages, books, code, images, or labeled records. What is in that data, and what is missing, shapes everything the model knows, how it writes, and which mistakes it makes.
- Ground truthGround truth is the verified, correct answer that an AI system's output is compared against, such as a human-labeled dataset or a confirmed real-world outcome. It is the reference standard used to train models and measure their accuracy.
- Reinforcement learning from human feedback (RLHF)RLHF is a training step in which human raters compare model responses, their preferences train a reward model, and the language model is then tuned to produce answers people rate highly. It is how raw text predictors became helpful assistants.
- Machine learning (ML)Machine learning is the branch of AI in which software learns patterns from examples instead of following rules a programmer wrote by hand. Nearly every modern AI tool, including chat assistants, is built on it.