Fine-tuning

Fine-tuning is the process of continuing the training of an existing large language model on a custom dataset, updating the model’s weights so it produces output more like the new data.

For brand voice work, fine-tuning is rarely the right answer. The training cost is high, the iteration loop is slow (days, not minutes), and the resulting custom model is locked to one base — when the base model gets better, your fine-tune doesn’t inherit the improvement until you re-train. Most "fine-tuned for your brand voice" claims in marketing tools are actually doing prompt-based brand conditioning, not real fine-tuning.

When fine-tuning does make sense: domains where the LLM does not understand the vocabulary out of the box (legal, medical, technical), or where the output format is unusual enough that even careful prompting can’t reliably produce it. For most brands, retrieval-augmented generation against a structured fingerprint produces equivalent quality at a fraction of the operational complexity.

"Fine-tuned on your brand" is often marketing-speak for "we use your samples in the prompt". Knowing the difference helps you ask the right question: is this real fine-tuning, and if so, what is the iteration cycle when you want to change something?