RAG vs Fine-Tuning: What Should You Actually Use?

The question comes up in almost every AI scoping conversation, usually phrased as though one is the advanced option and the other the shortcut. They are not on the same axis.

The short version: retrieval changes what the model knows. Fine-tuning changes how the model behaves. If your problem is knowledge, retrieval. If your problem is form, fine-tuning. Most business problems are knowledge problems.

What retrieval is for

Retrieval-augmented generation searches your content at question time and gives the model the relevant passages as context. The model answers from what it was handed.

This is the right tool when the answer exists somewhere in your documents, when that content changes, when you need to cite a source, and when different users are allowed to see different things. Permissions are a decisive point that gets overlooked: retrieval can filter by what the asker is allowed to read. A fine-tuned model cannot unlearn a document for one user.

Changes

What the model knows

Updating

Re-index — minutes

Citations

Native

Permissions

Filter at retrieval

Cost shape

Higher per query

What fine-tuning is for

Fine-tuning continues training a base model on your examples, adjusting its weights. It is how you teach consistent output format, a specific tone, a classification scheme with your own labels, or a narrow task you want done reliably and cheaply at volume.

It is not a way to load knowledge. Facts baked into weights cannot be cited, cannot be revoked per user, and go stale the moment the underlying document changes. Correcting one fact means preparing data and running another training job.

Changes

How the model behaves

Updating

Retrain — hours to days

Citations

Not possible

Permissions

Baked in for everyone

Cost shape

Higher upfront, cheaper per query

The decision, in one pass

1. 01Does the answer live in documents you control? Retrieval.
2. 02Does that content change monthly or faster? Retrieval, and do not fine-tune on it.
3. 03Must the answer cite a source? Retrieval.
4. 04Do different users see different data? Retrieval, with permission filters.
5. 05Is the problem that output format or tone is inconsistent? Fine-tuning — after you have tried a stricter prompt and structured output.
6. 06Is it one narrow high-volume classification, where latency and cost per call matter? Fine-tune a small model.

The thing to try before either

A surprising share of AI requirements are met by a good prompt, structured output, and a few examples in context. It costs a day to find out. Doing that first tells you which of the two real options you actually need, and sometimes it tells you that you need neither.

Fine-tuning to fix a knowledge problem is the most expensive way to get a stale answer.

And often, both

Mature systems frequently use retrieval for the facts and a fine-tuned or small specialised model for a narrow step inside the pipeline — routing, classification, extraction. The architecture is a pipeline, not a single choice, and the question is which tool handles which stage.