Tag: fine-tuning

Few-shot prompting handles most use cases better than fine-tuning. OpenAI requires minimum 10 training examples but meaningful gains typically need 50 to 100 or more. The return on investment calculation works in fewer scenarios than vendors admit.

Stanford and UC Berkeley research across twelve language models shows RAG vs fine-tuning is not about which is better. It is about data freshness, team capacity, and whether your knowledge changes daily or yearly.