RAG vs Fine-Tuning: What Do You Actually Need?

Two approaches come up again and again when teams want AI to know their business.

RAG: look it up first

Retrieval-augmented generation searches your documents for the relevant passages, then asks the model to answer using them. Answers can point back to the source, and updating knowledge is as easy as updating a document.

Fine-tuning: teach a style

Fine-tuning adjusts the model itself using examples. It helps with consistent tone, format, or specialized tasks, but it is slower to change and does not keep facts fresh.

Which one?

• Need answers from your changing documents? Start with RAG.
• Need a very specific style or output format? Consider fine-tuning.
• Not sure? RAG is usually the faster, cheaper first step.

Many successful projects never need fine-tuning at all.