What is fine-tuning?
Retraining a ready-made AI model on your own material. When you need it, when you don’t, and why most organisations get by perfectly well without it.
Fine-tuning is taking a pre-trained AI model and training it a little more on your own material, so it learns a specific style, jargon, or task. The result is your own model variant that carries the knowledge inside itself.
That sounds like something every organisation should do, but in practice it’s rarely the right first step. Fine-tuning is expensive, needs large amounts of good examples, and the model has to be redone whenever the material changes. For the most common need, getting the AI to answer based on your documents and terminology, there are simpler ways that update the moment your material does: pull in the right sources for every question (the technique is called RAG and has its own entry) and steer the answers with instructions and glossaries.
That’s how EuroWork works: the knowledge library turns your documents into sources for every colleague’s questions, specialists carry your instructions, and the terminology list controls word choices, all without retraining any model. Fine-tuning has its place, but that place is narrower than the brochures from vendors suggest.
Your employees are already using AI, often in private accounts where no one sees what gets pasted in. EuroWork gives the same AI help in one place where you are in control: sensitive data is caught before it is sent, processing stays in the EU, every answer shows its price and management sets the rules.
See how EuroWork works →