In the context of transformer architecture, what is the main purpose of fine-tuning a pre-trained model for a specific business application?

Fine‑tuning a pre‑trained transformer lets the model learn the specific language, patterns, and goals of a business task, making its predictions more relevant and accurate for that domain. It does this by continuing training on a smaller, task‑specific dataset while preserving the broad knowledge gained during the large‑scale pre‑training. This process teaches the model domain‑specific jargon, user intent, and desired output style, which improves performance and efficiency. For example, fine‑tuning a GPT model on a company’s customer‑support logs can make it answer product questions more quickly and correctly. In short, fine‑tuning adapts a general model to the precise needs of a particular business application.