Fine-Tuning
Taking an already-trained model and training it a little more on your own examples so it specializes in your task, tone, or domain. You adjust an existing model rather than start from scratch.
Think of It Like This
Hiring an experienced generalist and giving them a week of on-the-job training.
Fine-tuning starts from a pretrained model's weights and nudges them with a smaller, task-specific dataset — usually at a low learning rate so hard-won general knowledge isn't wiped out. It's how you bake in a house style or a niche skill that prompting alone can't reliably produce. When you mainly need the model to know new facts rather than adopt new behavior, retrieval is often the cheaper, faster choice.