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 adjust its internal weights to the patterns and vocabulary of a particular business domain, so it can give more accurate and relevant predictions. The pre‑trained model already knows general language structure, and fine‑tuning teaches it the specific jargon, customer intent, or compliance rules that matter for the task. By training on a smaller, domain‑specific dataset, the model learns to weight the right signals and ignore irrelevant noise. For example, a transformer trained on news can be fine‑tuned on customer support logs so it better classifies complaints and recommends solutions. This process improves accuracy, reduces errors, and makes the model useful for the company’s real‑world workflow.