Fine-tuning takes a pre-trained model and further trains it on a smaller, task-specific dataset to adapt it to a particular use case. Instead of building from scratch, you start with a model that already understands language and specialize it. A Moroccan bank might fine-tune an open-source LLM on its internal loan policies. Methods range from full fine-tuning to parameter-efficient approaches like LoRA and QLoRA.