RAG vs fine-tuning: which does your business actually need?

When companies want an AI feature, they often ask to fine-tune a model. Most of the time, that is not what they need.

Retrieval-augmented generation (RAG)

RAG keeps your knowledge in a searchable store and feeds the relevant parts to the model at answer time. It is ideal when your content changes often and you want grounded, citable answers.

Fine-tuning

Fine-tuning bakes behaviour into the model itself. It shines for fixed style, tone or narrow tasks, but it is slower to update and easier to get wrong.

A quick rule of thumb

• Answers from your docs that change weekly? Use RAG.
• Consistent tone or a specialized format? Consider fine-tuning.
• Not sure? Start with RAG. It is faster to ship and easier to trust.