Training a model on examples that are already labeled with the right answer, so it learns to predict labels for new data.
Supervised Learning underlies most traditional enterprise AI. Trained on curated, human-labeled examples (for instance, fraudulent and valid transactions), the model learns a decision boundary that separates the categories and can then classify new cases automatically.
See also Large Language Model, Fine-Tuning and Model Inference.