RAG vs fine-tuning: which one does your AI product actually need?

Almost every AI project we scope hits the same fork early: should we fine-tune a model on our data, or retrieve the right information at query time and give it to the model? The honest answer is that they solve different problems, and most products need retrieval first.

What RAG is good at

Retrieval-augmented generation (RAG) finds the most relevant pieces of your content, such as help articles, policies, product specs or past tickets, and places them in the prompt. The model answers from that context. It is the right default when knowledge changes often, when answers must cite sources, and when different users are allowed to see different data.

• Knowledge updates instantly: re-index a document and the next answer uses it.
• Answers can show sources, which builds trust and makes errors easy to spot.
• Access control is enforced at retrieval time, so users only see what they are allowed to.
• No training run, so iteration is fast and cheap.

What fine-tuning is good at

Fine-tuning changes how a model behaves rather than what it knows. It shines when you need a consistent format, tone or classification behaviour across thousands of requests, or when you want a smaller, cheaper model to match a larger one on a narrow task.

• Strict output formats and house style.
• High-volume classification or extraction where cost per call matters.
• Domain jargon the base model consistently misreads.

How we decide

We start with a strong base model, good prompts and retrieval, then build an evaluation set from real examples. Only if the evals show a gap that prompting and retrieval cannot close do we consider fine-tuning, and when we do, we usually keep retrieval too. Fine-tuning for knowledge alone is rarely worth the maintenance.

The part everyone skips: evaluation

Whichever path you take, the deciding factor is measurement. A few hundred representative questions with expected answers, scored automatically and reviewed by people, will tell you more than any architecture debate. If you are weighing this decision right now, our four-week AI Pilot is designed to answer it with your own data.