Fine-tuning takes a pre-trained model and continues training it on a focused set of your own examples. The model keeps its broad capabilities while adapting to your domain, terminology, format, or tone.

It is the right tool when prompting alone is not enough, for instance when you need a consistent output style, specialized classification, or behavior that is hard to describe in words but easy to show with examples. Techniques like LoRA and QLoRA make this far cheaper than full retraining.

arosplatforms reaches for fine-tuning deliberately, not by default. For most knowledge questions, retrieval is faster and cheaper to maintain. We fine-tune when the data and the measurable gain justify it, and we always pair it with evaluation to prove the improvement is real.