RAG vs Fine-Tuning: When Should You Use Each?

Teams often ask whether they should fine-tune a model on their data. The more useful question is: what is the model missing — knowledge, or behaviour?

Use retrieval when knowledge changes

If answers depend on documents that change — policies, product catalogues, tickets — retrieval-augmented generation keeps the model grounded and lets you cite sources.

Use fine-tuning when behaviour matters

If you need a consistent format, tone or a specialised task at lower cost and latency, fine-tuning a smaller model can outperform prompting a large one.

• Start with prompting and retrieval
• Build an evaluation set from real examples
• Fine-tune only when evaluations show a clear gap