Guide
Decisions & comparisons
RAG or fine-tuning — when to use which (and when to use both)
RAG adds knowledge to a model by searching your documents; fine-tuning changes the model's behavior itself. Most of the time you start with RAG.
- RAG = fresh knowledge from documents, with no change to the model.
- Fine-tuning = a baked-in style and format, more expensive to maintain.
- Most often: RAG first, fine-tuning only when RAG isn't enough.
What the difference is
Both methods help a model handle your data better, but they work in different places. RAG leaves the model unchanged and, before each answer, adds to the query the most relevant fragments of your documents. Fine-tuning goes deeper: it changes the model's weights, baking style, format or knowledge directly into it.
In short: RAG changes what the model sees for a given question. Fine-tuning changes what the model is.
When to choose RAG
RAG is the default choice when knowledge changes often or has to be grounded in sources. You turn documents into embeddings and store them in a vector database; updating the knowledge means swapping the documents, not retraining.
- Knowledge changes weekly or monthly.
- The answer has to cite its source.
- You're working with company data you don't want to feed into training.
When to choose fine-tuning
Fine-tuning wins when it's about behavior rather than the freshness of knowledge: a repeatable tone, a strict output format, or a narrow domain that can't be comfortably fit into the context. It requires preparing training data and maintaining a separate version of the model.
How to choose — a quick comparison
| Criterion | RAG | Fine-tuning |
|---|---|---|
| Knowledge freshness | High (swap the documents) | Low (you have to retrain) |
| Cost to implement | Lower | Higher |
| Style and format | Limited | Strong |
| Sources in the answer | Natural | Hard |
| Best for | Living domain knowledge | A baked-in style, a narrow domain |
Operator's rule: start with RAG, measure quality with evaluation, and add fine-tuning only where RAG genuinely falls short.
The most common pattern: both at once
In practice, mature deployments combine the two: fine-tuning sets the style and format, while RAG supplies the current knowledge. Good document chunking and solid evaluation make a bigger difference here than the choice of method alone.
Terms in this guide
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Frequently asked questions
- Which is cheaper: RAG or fine-tuning?
- Usually RAG — it requires no model training and no separate version to maintain, and you update the knowledge simply by swapping the documents.
- Can you combine RAG and fine-tuning?
- Yes. A common pattern is fine-tuning for the style and format of the answer plus RAG for current domain knowledge.
- When does fine-tuning have the edge?
- When you care about a repeatable style, a strict output format, or a narrow domain that can't be conveniently supplied in the context.