Fine-tuning

Fine-tuning is the process of further training a pre-existing language model on a specialized dataset to adapt it to a specific domain, style, or task. It modifies model weights to better reflect that corpus. For organizations with recurring AI message patterns, fine-tuning can reduce prompt complexity, improve accuracy, and lock in brand voice, but it also introduces data governance, monitoring, and retraining responsibilities. Fine-tuning is the process of further training a pretrained language model on a specialized dataset so it better reflects a specific domain, style, or task. The model’s parameters are adjusted using curated examples. For organizations that send large volumes of Ai Messages, fine-tuning can reduce prompt complexity and improve quality, but introduces responsibilities around data governance, evaluation, and lifecycle management.