You're building an NLP system that needs to answer or generate text using domain-specific information. In some cases, you can inject relevant context at inference time. In others, you may want to adapt the model itself through supervised fine-tuning.
How do you decide when to fine-tune a model versus rely on context injection?
When RAG is preferable to changing model weightsWhen supervised fine-tuning improves behavior more than promptingHow to reason about evaluation, hallucination risk, and operational trade-offsWhen a hybrid design is the right answer