RAG vs Fine-Tuning: When to Use Which

RAG and fine-tuning both improve how a model answers questions, but they do it in different ways. RAG pulls in external documents at answer time; fine-tuning updates the model's weights on examples. Choosing between them (or using both) depends on what you need: fresh data, provenance, control over updates or consistent style and format.

RAG in One Line

RAG retrieves relevant passages from your data store and passes them to the model as context. The model stays unchanged; you change what you feed it. So you can point it at new or updated docs, internal wikis or tickets without retraining.

Fine-Tuning in One Line

Fine-tuning trains the model on your own examples (input–output pairs or longer sequences). The model's weights change. It gets better at the tasks and style you trained it on, but it does not automatically "see" new documents unless you add them to the training data and retrain (or use something like continual pre-training).

When RAG Fits

• You need answers grounded in specific, up-to-date or proprietary documents (policies, contracts, manuals, tickets).
• You want citations: users should see which doc or chunk supported the answer.
• Content changes often and you want to reflect that by updating the index, not the model.
• You have a large corpus and cannot fit it all in the context window; you need retrieval to select the right subset.

When Fine-Tuning Fits

• You need consistent format or tone (e.g. always a certain JSON shape, or a formal/informal style).
• The knowledge is implicit in patterns (e.g. task-specific reasoning, domain jargon) and is best learned from examples.
• You want the model to "know" things without stuffing them into every prompt (e.g. product names, internal codes).
• You are optimizing for latency or cost and want a smaller or cheaper model that behaves like a larger one on your tasks.

Trade-Offs at a Glance

RAG: easier to update (re-index), built-in sources, good for large and changing corpora. You pay per retrieval + generation and need to design chunking and retrieval. Fine-tuning: no retrieval step at inference, but you need data, training runs and a strategy for when to retrain. Knowledge is "baked in" so updates mean new training.

Using Both

Many systems use both: a fine-tuned model for format and domain behaviour, and RAG to inject the latest or most relevant documents. For example, fine-tune for "always cite sources and use this tone," then use RAG to supply the chunks the model cites. The fine-tuned model is better at using that context; RAG keeps the context accurate and current.

For more on building the retrieval side, see RAG Introduction and Retrieval Methods in RAG.