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What is the difference between RAG and fine-tuning?

RAG (retrieval-augmented generation) feeds a model your data at question time, so it answers from your documents without changing the model. Fine-tuning retrains a model on your examples so it internalizes a style or task. RAG is for knowledge, fine-tuning is for behavior.

For most businesses, RAG is the right starting point. It keeps your information current (update the documents, not the model), it's cheaper, and it lets the model cite sources. It's how you build a support bot that answers from your actual help center.

Fine-tuning earns its cost when you need a consistent voice, a specialized format, or a narrow task done the same way every time, and you have enough good examples to teach it. Many production systems use RAG for the facts and light fine-tuning for the tone.

Updated July 2026

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