Researchers have discovered that retraining small portions of AI models can be a cost-effective way to prevent forgetting and ensure model performance. When enterprises fine-tune models, they may unintentionally cause the model to lose some of its capabilities. This phenomenon, known as forgetting, can hinder the model's ability to perform certain tasks it previously learned. By retraining only specific parts of the model, organizations can address this issue and maintain the model's performance without incurring high costs. The University of California, Berkeley, conducted research in this area, highlighting the importance of strategic retraining to optimize AI model functionality. This approach not only enhances model efficiency but also helps businesses avoid the potential drawbacks of forgetting. By implementing these findings, enterprises can better tailor their AI models to real-world tasks while minimizing the risk of performance degradation.