In many AI systems, a pre-trained model is initially trained on a broad set of data to learn general features. This model is then fine-tuned on a specific task using a smaller, task-specific dataset. For instance, a model might first be trained on a vast collection of text from the internet, and then fine-tuned for sentiment analysis. This approach saves computational resources and can lead to better performance as it utilizes previously learned knowledge.