Fine-tuning is the process of further training a pre-trained AI model on a smaller, task-specific dataset to adapt it for particular use cases. Unlike training from scratch, fine-tuning starts with a model that already understands language and adjusts its weights to excel at specific tasks like medical diagnosis, legal analysis, or customer support. Common approaches include full fine-tuning, LoRA (Low-Rank Adaptation), and QLoRA, which reduce computational costs. Fine-tuning can improve accuracy, reduce latency, and lower costs compared to using general-purpose models with complex prompts.
Frequently Asked Questions
What is fine-tuning in AI?
Fine-tuning is adapting a pre-trained AI model for specific tasks by training it further on specialized data. It is faster and cheaper than training from scratch.
When should I fine-tune vs use prompting?
Fine-tune when you need consistent performance on a specific task, lower latency, or reduced costs at scale. Use prompting for prototyping, diverse tasks, or when you lack training data.