Example Concept: Fine-tuning a language model typically involves using a pre-trained model and training it further on a smaller, task-specific dataset. This process adjusts the model's weights to better capture the nuances of the new data, improving its ability to generate contextually appropriate responses. Fine-tuning requires selecting a suitable dataset, setting hyperparameters like learning rate and batch size, and using a framework like TensorFlow or PyTorch to implement the training loop.