Fine-tuning is the process of further training a pre-trained AI model on a smaller, domain-specific dataset to adapt it for specific tasks. In 2026, parameter-efficient methods like LoRA and QLoRA dominate due to their cost-effectiveness and ability to reduce trainable parameters by 10,000x.