# Overfitting

Concept · [Building with AI](https://ecosystem.samcarlton.com/ai)

Also called: overfit, overfitted, underfitting, memorization

When a model learns its training examples too closely, including their quirks, and then performs poorly on new data. It is like memorizing practice test answers without understanding.

**For example:** A model [fine-tuned](https://ecosystem.samcarlton.com/fine-tuning) on 50 support [tickets](https://ecosystem.samcarlton.com/backlog) repeats their exact wording and fails on questions phrased differently.

## Connects to (4)

- [Training](https://ecosystem.samcarlton.com/training): Teaching an AI model by showing it a very large number of examples before anyone uses it.
- [Fine-tuning](https://ecosystem.samcarlton.com/fine-tuning): Extra training of an existing model on a smaller, narrower set of examples, so it suits one task or style.
- [Evals](https://ecosystem.samcarlton.com/evals): Tests that check whether the outputs of a model or an AI product meet the criteria its builders set.
- [Loss function](https://ecosystem.samcarlton.com/loss-function): A formula that scores how wrong a model's output is compared with the right answer.

Map view: https://ecosystem.samcarlton.com/overfitting