RAG vs Fine-Tuning

Use RAG to teach a model new facts. Use fine-tuning to teach it new behavior.

This is the most common architecture decision people get wrong. Most teams that say "we should fine-tune" actually need RAG. The opposite is true less often, but it happens.

	RAG (Retrieval-Augmented Generation)	Fine-Tuning
What it's for	Knowledge that changes (your docs, products, policies).	Style, format, narrow task behavior.
Update frequency	Real-time. Add a doc → it's available immediately.	Slow. Each update = re-train (hours-days).
Cost to set up	Low to medium. Vector store + chunking pipeline.	Medium to high. Need clean labeled examples.
Cost to run	Higher inference (longer prompts).	Lower inference (smaller prompts).
Hallucination	Low — the model has the source in front of it.	Same as base model. Doesn't fix factual errors.
Citations	Easy. Cite the retrieved document.	Hard. Model can't point to a source.
Data needed	Just your documents.	500+ high-quality input/output pairs.

Pick RAG (Retrieval-Augmented Generation) when

Use RAG when: you need the model to answer using your data, the data changes, citations matter, or you have unstructured docs.

Pick Fine-Tuning when

Use fine-tuning when: you need a specific output style or format that prompting won't reliably produce, you have a narrow well-defined task, and you have a lot of clean examples.

Bottom line

In practice we ship RAG 90% of the time. Fine-tuning is a tool for narrow, high-volume, format-specific tasks — not a general "make the model smarter" lever.

Need help picking — or stitching them together?

We do this for clients every week. Bring us the workflow, we'll bring the architecture.

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Glossary