Overfitting

Overfitting is when an AI model performs well on the data it was trained on but poorly on new, unseen data — because it learned the specific patterns of the training set rather than generalizable principles. An overfitted model is essentially memorizing rather than learning. For business users, overfitting becomes relevant when building or fine-tuning custom AI models: a model fine-tuned on a small dataset might appear to work perfectly on examples from that dataset but fail on new inputs.

The practical fix is ensuring training data is diverse and representative.