New research from the University of [insert university name] reveals that retraining only select components of AI models can significantly reduce costs while preserving their existing knowledge. In the realm of fine-tuning large language models (LLMs), organizations often face the challenge of models "forgetting" previously learned tasks after adjustments are made for specific applications. This study suggests a more efficient approach, allowing enterprises to customize AI without sacrificing the model's valuable capabilities. By focusing on smaller segments of the model during retraining, businesses can enhance performance for particular tasks while maintaining proficiency in others. This innovative strategy not only streamlines the customization process but also minimizes the risk of losing critical information, making it a game-changer for industries looking to leverage AI effectively. As organizations increasingly adopt AI technologies, this research offers a roadmap to optimize model performance and ensure that valuable skills remain intact, ultimately leading to more productive and cost-effective AI deployments.