Reusing what a model learned on one task as the starting point for a related task, so the new task needs less data and training.
Transfer Learning lets organizations take a large open-source model trained on broad public data and apply its general language capability to specialized, proprietary enterprise data, which saves compute while still reaching high accuracy.
See also Large Language Model, Fine-Tuning and Model Inference.