Example Concept: Fine-tuning a pre-trained NLP model typically involves the following steps: First, select a pre-trained model that fits your task, such as BERT for text classification. Next, prepare your task-specific dataset, ensuring it is formatted correctly for the model. Then, adjust the model's architecture if necessary (e.g., adding a classification layer). Finally, train the model on your dataset using a smaller learning rate to prevent overfitting, and validate its performance to ensure it generalizes well to unseen data.