Imagine a competent consultant. To adapt them to your company, you have two options. Either you give them access to your documentation and they consult it when needed: that's RAG. Or you train them for months until they think like your organisation: that's fine-tuning. These two approaches don't solve the same problem, and confusing them is the most common mistake in enterprise AI projects.
We've explained RAG and fine-tuning separately. Here's the practical question: which one do you pick?
What each one does
RAG, or retrieval-augmented generation, doesn't modify the model. When you ask a question, a system searches your documents for relevant passages, adds them to the context, and the model answers based on them. The search mechanism relies on embeddings.
Fine-tuning modifies the model itself, by continuing its training on your examples. The knowledge is no longer consulted; it's baked into the parameters.
The question that decides it
One question settles it in the vast majority of cases: do you want the model to know facts, or to behave differently?
If you want it to answer based on your internal procedures, your product catalogue, your knowledge base: that's facts, so RAG.
If you want it to adopt a very precise output format, a particular tone, or to handle a repetitive task with perfect consistency: that's behaviour, so fine-tuning.
The classic mistake is trying to teach knowledge via fine-tuning. It's costly, it works poorly, and it creates a major problem: the model mixes what it's learned with what it already knew, without being able to cite its source.
Immediate updates: you change a document, the answer changes on the next query. With fine-tuning, you have to retrain.
Traceability: the system can cite the passage used, making the answer verifiable.
Access control: you can filter documents based on user permissions, impossible with a model that has absorbed everything.
Cost: far cheaper to set up and maintain.
Reversibility: removing a document is instant; unlearning isn't.
When fine-tuning is genuinely justified
Three situations, and they're rarer than you'd think.
A strict, repetitive output format. If you need to produce a very precise structure thousands of times, a fine-tuned model will do it more reliably and with fewer instruction tokens.
A domain with highly specific vocabulary. Certain technical jargons or underrepresented languages benefit from deep adaptation.
Cost reduction at scale. A small fine-tuned model can match a large generic model on a narrow task, for far less per query. That's the strongest argument when volume is high.
The practical answer
For nearly all enterprise projects: start with RAG. It's faster to set up, cheaper, easier to maintain, and it meets the real need in the vast majority of cases.
And know that the quality of a RAG system depends far more on how you chunk your documents and the quality of the search than on the model used. Projects that fail almost always fail there, not on the model choice. An excellent model receiving the wrong passages produces an excellent answer that's beside the point.