This is the process of taking a pre-trained LLM and further training it on a smaller, specific dataset to adapt it for a particular task or to improve its performance. By finetuning, we are adjusting the model’s weights based on our data, making it more tailored to our application’s unique needs. The main challenge of this approach is the resource-intensive nature of collecting and labeling training data, as well as the computational power required to train the model. It also needs professional data science skills to optimize the objective function, making it more suitable for experienced practitioners or teams with dedicated resources.