Essentially, fine-tuning refines the general knowledge of the LLM to make it more effective for a targeted use case.
Fine-tuning a large language model (LLM) involves taking a pre-trained model and training it further on a specific dataset or task. This process allows the model to adapt to particular domains or improve its performance on specialized tasks by adjusting its parameters based on the new data.
