Supervised learning trains an AI model using labeled data — examples where both the input and the correct output are known. The model learns to predict the correct output for new inputs based on the patterns it identified in the labeled examples.
It is widely used for classification, intent recognition, and prediction tasks where sufficient labeled training data is available. The quality and representativeness of that labeled data is the primary constraint on how well the resulting model performs in production.
