Question 1

Should you use RAG or fine-tuning for enterprise AI?

Accepted Answer

Use retrieval-augmented generation (RAG) when answers depend on company facts that change, such as policies, docs or account data, because it pulls current information at query time and can cite sources. Use fine-tuning to change a model's style, format or narrow behavior. Most customer support and enterprise assistants rely on RAG, sometimes combined with light fine-tuning.

Question 2

Is RAG better than fine-tuning?

Accepted Answer

For factual, changing knowledge, usually yes. For behavior and style, fine-tuning can help.

Question 3

Can you combine RAG and fine-tuning?

Accepted Answer

Yes. Many systems fine-tune for format and use RAG for facts.

Question 4

Is fine-tuning expensive?

Accepted Answer

It costs more upfront than RAG and must be redone as data changes.

	RAG	Fine-tuning
Updates knowledge	Instantly	Requires retraining
Citations	Yes	No
Changes style	Somewhat	Strongly
Cost to update	Low	Higher
Data privacy	Data stays in your store	Data goes into training
Best for	Facts, policies, account data	Tone, format, specialized tasks

Should you use RAG or fine-tuning for enterprise AI?

Comparison

Beyond RAG

Frequently Asked Questions