TL;DR
RAG and fine-tuning are two ways to adapt a model to your needs. RAG (retrieval-augmented generation) feeds the model relevant external knowledge at query time, leaving the model unchanged. Fine-tuning further trains the model's weights on your data, baking knowledge and behaviour in. RAG is for changing facts; fine-tuning is for changing behaviour. Many teams use both.
Why they get compared
Both make a general model work better for your use case, so they are framed as competing options. They are, but they change different things: RAG changes what the model knows at the moment it answers, and fine-tuning changes how the model behaves for good.
What is RAG?
Retrieval-augmented generation adds knowledge to a model without retraining it. At query time, a retrieval step searches an external index, usually a vector database, for text relevant to the question and adds it to the prompt. The model answers from that retrieved context. The weights never change; the knowledge lives outside the model, so you update it by changing the documents, not the model.
RAG is the right tool when knowledge changes often or must be traceable: product docs, policies, a knowledge base, anything where what the model knows needs to stay current and cite a source.
What is fine-tuning?
Fine-tuning continues training a pre-trained model on a domain-specific dataset, adjusting its weights. It changes the model itself: its tone, its format, its skills, and to a degree its knowledge. Once fine-tuned, the behaviour is built in, with no retrieval step required, but updating it means training again on new data. It is close kin to instruction tuning, which trains a model to follow instructions more reliably.
Fine-tuning is the right tool when you need consistent behaviour rather than fresh facts: a specific output format, a house style, or a specialised classification or extraction skill the base model does not reliably perform.
RAG vs fine-tuning: the core difference
RAG changes what the model can see at answer time; fine-tuning changes what the model is. One is external and updatable, the other is internal and durable.
| RAG | Fine-Tuning | |
|---|---|---|
| Changes | What the model sees at query time | The model's weights |
| Knowledge lives | Outside the model, in an index | Inside the model |
| Updating | Edit the documents | Retrain on new data |
| Best for | Fresh, changing, traceable facts | Consistent behaviour, style, skills |
| Freshness | As current as the index | Fixed until the next training run |
| Traceability | Can cite the retrieved source | Hard to trace to a source |
| Cost profile | Per-query retrieval and longer prompts | Upfront training, cheaper prompts |
How they combine
They are not mutually exclusive. A common setup fine-tunes for behaviour and uses RAG for knowledge:
Fine-tune the model for format and skill -> then at query time RAG adds the current facts -> grounded, on-brand answer
Fine-tuning sets how the model responds; RAG keeps what it responds with up to date.
A concrete pairing: a support assistant fine-tuned to always answer in a terse, numbered format, with RAG pulling the current policy text on each question. The fine-tune fixes the shape of every answer; RAG makes sure the facts are today's, not last quarter's. Change the policy and the assistant is current with no retraining, while the tone stays put because it lives in the weights.
Which should you use?
Reach for RAG when the problem is knowledge: facts that change, or answers that must cite a source. Reach for fine-tuning when the problem is behaviour: a format, a style, or a skill the base model does not hold. When you need both a consistent voice and current facts, combine them, fine-tune for the how and RAG for the what. Start with RAG; it is cheaper to build and change, and it covers the most common need.
RAG and fine-tuning both aim at the same target, an answer that is correct and grounded, so the real question is whether they got there. Measuring that, across whichever approach you use, is evaluation, and it is what Prefactor scores on every run.