RAG or fine-tuning: how to decide for your case

For your case, choose RAG when the information changes frequently and must be citable, and fine-tuning when you want to fix a tone, a format or a stable task into the model itself. RAG gives it access to your data at the moment of answering; fine-tuning retrains it with that data. It is not a choice of sides, but of which part of the problem each technique solves.

What each technique does

Fine-tuning retrains a model with your examples so it internalizes a style, a format or a way of solving a task. The knowledge is "baked" into the model's weights. RAG does the opposite: it leaves the model intact and gives it access to your sources at the moment of answering, retrieving the relevant fragments and generating the answer over them. One changes how the model answers; the other changes with what information it answers.

Decide by the freshness of the information

The question that decides fastest is: how often does your data change? If the information updates —prices, inventory, documentation, internal regulations—, RAG is almost always the answer, because it connects the model to the live source without retraining anything. Fine-tuning, by contrast, freezes what the model knew at the moment of training: for data that changes, it would force retraining over and over, which is slow and expensive.

Decide by traceability and control

In an organization it is rarely enough for the answer to be correct: you have to be able to explain where it comes from. RAG makes traceability easier because it can cite the documents it used, something decisive in sensitive or auditable processes. A fine-tuned model generates the answer from its weights, with no source to point to. If you need to verify and cite the origin of each answer, RAG starts with an advantage.

Decide by cost and maintenance

Fine-tuning has an entry cost —preparing quality data and retraining— and a recurring cost every time the information changes. RAG shifts the effort to building and maintaining good retrieval over your sources, but avoids retraining with every update. For most enterprise cases, where the information moves, RAG tends to be cheaper to maintain; fine-tuning pays off when the task is stable and the style matters more than the data.

When to combine them

The usual thing is not to choose one and discard the other. A well-designed system can fine-tune the model so it answers in the format and tone you need, and use RAG to feed it the current information it must cite. The useful question is not "RAG or fine-tuning?" but "which part of my problem is style and a stable task, and which part is information that changes?".

How we approach it at Codara

At Codara we decide the technique from your case —data freshness, the need to cite, maintenance cost— and integrate it into Codara's agentic orchestration layer connected to your sources, so the system answers with verifiable data and your team runs it without us.