Can you use both at the same time?

Yes: a model fine-tuned on a style or a task can also rely on RAG for its factual knowledge. The two techniques complement each other.

Is fine-tuning more expensive than RAG?

Generally yes: it requires a training dataset, compute for retraining, and a new cycle with every update. RAG is updated simply by changing the source documents.

Is RAG enough for a first project?

In the vast majority of cases, yes, especially if the need is to surface internal knowledge that is up to date and traceable.

When does fine-tuning become necessary?

When the need is a precise, repeated behaviour (a response format, a writing style, a specialised classification task) that prompting and RAG can no longer deliver reliably.

RAG or fine-tuning: which one for your AI project

RAG and fine-tuning both adapt a language model to your context, but not in the same way: RAG connects the model to your up-to-date documents without retraining it; fine-tuning changes the model itself. The choice depends on what you are trying to achieve, and the two are often combined.

Two techniques, two different problems

RAG (Retrieval-Augmented Generation) and fine-tuning answer two distinct questions. RAG answers "how do you give the model access to knowledge it does not have, and that changes often?". Fine-tuning answers "how do you get the model to adopt a specific style, vocabulary or behaviour?". Confusing the two leads to choosing the wrong solution, or to over-investing in one when the other would do.

How each one works

RAG connects the model to your documents at the moment a question is asked: it retrieves the relevant passages and hands them to the model, which writes an answer grounded in those sources, with references. Nothing is retrained; updating the knowledge base is enough to update the answers. Fine-tuning, on the other hand, retrains the model on a set of examples so that it durably adopts a specific format, tone or task. The knowledge it acquires is frozen in its weights, and cannot be consulted or traced like a source.

The decision table

Situation	Recommended solution
The knowledge changes often (procedures, contracts, catalogue)	RAG
You need traceable, verifiable sources	RAG
You want a consistent tone, format or style on a repeated task	Fine-tuning
You have little proprietary data	RAG, as fine-tuning needs more data
The need combines both (answer from your sources, in your tone)	RAG and fine-tuning combined

Why RAG is often the best place to start

RAG is faster to set up, cheaper to maintain (updating a document is enough, no retraining needed), and more transparent (every answer cites its sources, which reduces the risk of hallucination). Fine-tuning becomes relevant when the need concerns the model's behaviour rather than its knowledge, or when a well-built RAG is no longer enough to achieve the consistency you are after. In most of the business projects we run, a diagnostic is enough to decide before investing in either one.

Frequently asked questions

Can you use both at the same time?: Yes: a model fine-tuned on a style or a task can also rely on RAG for its factual knowledge. The two techniques complement each other.
Is fine-tuning more expensive than RAG?: Generally yes: it requires a training dataset, compute for retraining, and a new cycle with every update. RAG is updated simply by changing the source documents.
Is RAG enough for a first project?: In the vast majority of cases, yes, especially if the need is to surface internal knowledge that is up to date and traceable.
When does fine-tuning become necessary?: When the need is a precise, repeated behaviour (a response format, a writing style, a specialised classification task) that prompting and RAG can no longer deliver reliably.

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