Your AI doesn't know your refund policy. RAG and fine-tuning both fix that — but they solve different problems, and mixing them up is the most common mistake in AI engineering.
RAG retrieves relevant passages from your documents at answer time and passes them to the model as context. The model itself is never touched. Change a document, and the next answer changes with it.
Fine-tuning updates the model's parameters using curated training examples. It shapes how the model responds — style, format, task behavior — before anyone asks a question.
The practical rule:
→ RAG for knowledge that changes
→ Fine-tuning for behavior that should be consistent
What fine-tuning will NOT do is keep up with your changing documents. Baking facts into weights means retraining every time something changes. New information still has to be retrieved.
And these aren't rivals — plenty of production systems use both: fine-tuned for the house voice, RAG for the current facts.
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