AI model fine-tuning is the process of further training a pre-trained language model on domain-specific data to improve its performance for particular use cases. Fine-tuning adapts general-purpose AI capabilities to specialized tasks and terminology.
How does fine-tuning differ from prompt engineering?
Prompt engineering guides model behavior through instructions provided at runtime. Fine-tuning actually modifies the model’s underlying parameters using training examples. Prompt engineering is faster and more flexible; fine-tuning can produce more consistent specialized behavior but requires more data and computational resources.
Why does fine-tuning matter for voice agents?
Fine-tuned models can learn industry-specific vocabulary, preferred response styles, and nuanced handling of domain situations. For high-volume use cases, fine-tuning can also improve efficiency by reducing the length of system prompts needed to achieve desired behavior.
Fine-tuning in practice
A mortgage company fine-tunes a model on thousands of successful loan consultation transcripts. The resulting model understands specialized terminology like “debt-to-income ratio” and “rate lock” without explanation, follows their specific qualification flow, and maintains their preferred conversational tone consistently.