What is Fine-Tuning?
Fine-tuning is the process of taking a pre-trained language model and continuing its training on a specific dataset to specialize it for particular tasks, domains, or behaviors. This approach leverages the general language understanding and capabilities learned during pre-training while adapting the model to excel at more specific applications. Fine-tuning modifies the model's parameters through additional training, creating a customized version optimized for the target use case.
The fine-tuning process typically uses much smaller datasets and fewer training steps than pre-training, as the model already possesses broad language capabilities. Common fine-tuning objectives include adapting models to specific domains (medical, legal, technical), teaching particular output formats or styles, improving performance on specific task types, or aligning behavior with organizational requirements. The training data usually consists of input-output pairs that demonstrate desired behavior.
Fine-tuning offers advantages over prompting-based approaches for certain applications: it can encode complex behaviors that are difficult to specify through prompts alone, reduce the need for lengthy prompts (lowering costs and latency), and achieve better performance on specialized tasks. However, it requires technical expertise, computational resources, training data, and ongoing maintenance as requirements evolve. Many modern AI systems combine fine-tuned models with prompting techniques to leverage the strengths of both approaches.