RAG vs fine-tuning: choosing the right approach for enterprise AI

The distinction that matters

RAG gives a model relevant information at the moment of a request. Fine-tuning changes how a model tends to respond by training it on examples.

One primarily supplies knowledge. The other primarily shapes behaviour. Almost every confused AI architecture starts by blurring that line.

Use RAG when

• Answers must draw on private or frequently changing information
• Users need references back to source documents
• Access should follow existing document permissions
• Knowledge must be added or removed without retraining
• The use case depends on policies, manuals, contracts, records or product information

Consider fine-tuning when

• The required output format is highly consistent and specialised
• The model must learn a repeated classification or transformation pattern
• Prompting alone does not produce stable enough behaviour
• A large, high-quality set of representative examples already exists
• Evaluation shows tuning measurably improves the target task

When both may be appropriate

A tuned model can learn how to perform a domain-specific task while retrieval supplies the current facts needed to complete it. The combination is legitimate — but only when each technique has a separate, demonstrated purpose. If you cannot articulate what each one is fixing, you are paying for both and benefiting from neither.

What neither approach fixes

Neither technique rescues a system from:

• Poor source data
• Missing access controls
• Ambiguous workflows
• Weak or absent evaluation
• Unsafe tool permissions
• No human ownership of the outcome

A decision rule

If the problem is “the model does not know our current information”, start by evaluating RAG.

If the problem is “the model does not perform the task in the required way”, evaluate prompting and structured outputs before you consider fine-tuning. The cheapest intervention that works is the right one.