Example Concept: Fine-tuning a large language model typically involves using a pre-trained model and further training it on a smaller, domain-specific dataset. This process adjusts the model's weights to better capture the nuances of the niche topic. The steps include preparing your dataset, selecting a suitable pre-trained model, and using a framework like TensorFlow or PyTorch to perform the fine-tuning. During this process, you may need to adjust hyperparameters such as learning rate and batch size to optimize performance.