Fine-Tuning vs RAG: Which Should You Use?

Fine-tuning changes how a model behaves. Use it to enforce a tone, output format, classification method, or specialized workflow across many prompts. It is less suitable for storing facts that change frequently.

RAG retrieves relevant documents at request time and adds them to the prompt. Use it for product catalogs, policies, research libraries, or private knowledge bases where information must stay current and traceable.

Start with RAG when the main problem is missing knowledge; choose fine-tuning when the model knows enough but responds inconsistently. For many CinderHub workflows, combining RAG for facts with fine-tuning for style delivers the strongest result.