Example Concept: Fine-tuning a large language model involves adapting a pre-trained model to a specific task using a smaller dataset. Key practices include using techniques like transfer learning, where the model retains its pre-trained weights and only a few layers are adjusted. Regularization methods such as dropout and weight decay can help prevent overfitting. Additionally, employing data augmentation strategies and monitoring validation performance closely are crucial for achieving optimal results.