In addition, the model leverages a uniquely developed data-augmentation technique to generate responses optimized for user-specific applications.

Using supervised fine-tuning (SFT) to introduce even a small amount of relevant data to the training set can often lead to strong improvements in this kind of "out of domain" model performance.