Fine-tuning

You can sharpen a general model by training it further on your examples: your writing style, your type of classifications, your jargon. The model then learns the behavior you want, without you having to explain it in the instruction every time.

Fine-tuning is just not the first dial you should turn. For knowledge that is current or company-specific, RAG usually works better, is cheaper and quicker to update. Fine-tuning pays off mainly when you have a fixed, repeated task where the tone or format needs to sit very precisely.

Our rule of thumb: start with good prompts and RAG, measure with evals, and only bring in fine-tuning once the numbers show it genuinely adds something. Otherwise you pay for complexity you do not need.