Businesses often ask whether they need RAG or fine-tuning. The answer depends on the problem. If the system needs to answer from changing company knowledge, RAG is usually the better first choice. If the system needs a model to consistently follow a specialized style or pattern, fine-tuning may help later.
RAG retrieves relevant company information at answer time. This makes it useful for policies, manuals, product documentation, support content, and internal knowledge bases. The knowledge can be updated without retraining the model.
Fine-tuning changes model behavior through training examples. It can improve consistency, format, or domain-specific responses, but it does not automatically solve access control, citations, or fresh knowledge.
For most companies, the production questions are practical: Where is the knowledge? Who can access it? How will answers cite sources? How will quality be evaluated? Those questions usually point to RAG first.
Fine-tuning can be valuable, but it should not be used as a shortcut around content quality, retrieval design, or governance.