Transfer learning in AI gives developers a practical starting point by adapting a model that has already learned useful patterns to a related problem, rather than training every model from scratch. A vision model trained on a broad image collection may become the foundation for detecting damaged components on a factory line, while a language model trained on general text can be adapted for tasks like document classification or domain-specific search. This approach changed AI development because the most expensive learning stage no longer had to be repeated for every application, allowing smaller teams to build credible models with less task-specific data and compute. The advantage has limits, as a pretrained model is useful only when its existing knowledge transfers well to the new task. Transfer learning connects a source domain and source task with a target domain and target task, which need enough shared structure to remain useful. It is broader than fine-tuning, as knowledge may be transferred through learned features, pretrained parameters, selected layers, training examples, or small modules. Most projects begin with a pretrained checkpoint, where the original task-specific output layer is often replaced with a new head, and the pretrained backbone is frozen. If performance is limited, the team can unfreeze part of the backbone and continue training with a low learning rate to avoid overwriting useful representations.