Researchers have discovered that by retraining only specific areas of AI models, costs can be reduced and the risk of forgetting learned tasks can be minimized. When enterprises fine-tune models, it can lead to the loss of certain abilities in large language models (LLMs). This phenomenon, known as forgetting, occurs when models no longer perform tasks they were previously trained on. The University of California, Berkeley conducted a study that highlights the importance of strategic retraining to maintain model performance while avoiding forgetting. By focusing retraining efforts on targeted areas rather than the entire model, businesses can achieve cost savings and ensure that AI models retain their learned capabilities over time. This approach not only enhances the efficiency of AI systems but also improves their overall performance and reliability in real-world applications.