Fine-tuning vs RAG

Fine-tuning retrains a model with your data; RAG gives it access to your data when answering. You choose based on freshness, cost and control of information.

Two different approaches

Fine-tuning adjusts the weights of the model itself from examples, so it learns a style or behavior that stays fixed until the next retraining. RAG, by contrast, keeps the model intact and connects your information sources at the moment of answering, retrieving the relevant context on each query.

How to decide

They are not mutually exclusive and are often combined. If the information changes frequently or must be verifiable, RAG usually fits better; if what you need is consistent behavior or format, fine-tuning adds more. Maintenance cost, data freshness and the need for traceability tip the decision.

How we approach it at Codara

We choose the technique to fit your case, not the other way around: when we build an Agentic OS on Codara's own platform, we combine RAG, fine-tuning or both, depending on what data freshness, cost and the control your organization needs require.