Fine-tuning
What does it mean?
Fine-tuning is further training an existing AI model on your own examples so that it better fits a specific task, tone or domain. The model then learns patterns from your data, on top of what it already knew. For most organisations fine-tuning is not the first step — smart prompt design and RAG often already solve a lot.
Fine-tuning is useful when you consistently need a specific style or structured output that is hard to achieve with instructions alone. It does take preparation: good example data, evaluation and maintenance.
In practice we usually start with prompts and RAG, and only consider fine-tuning when it demonstrably adds value. That keeps cost and maintenance manageable.
From concept to application?
Book a no-obligation call. We translate these terms into what they concretely mean for your organisation.