“Should we use RAG or fine-tuning?” is a question that sounds technical but has a practical business answer. Both ground AI in your reality, but in different ways — and for most businesses, one is the clear starting point. Here’s the plain-English version, and how dgm decides. (dgm implements osFoundry, a separate company’s platform — we are not osFoundry.)

What each one is

  • RAG (retrieval-augmented generation) connects a model to your data so it retrieves relevant information at query time and grounds its answers in it — without changing the model. It adds knowledge.
  • Fine-tuning adjusts the model itself by training it on examples to change its behavior, tone, or format. It shapes behavior.

The simplest way to remember it: RAG adds knowledge; fine-tuning shapes behavior.

Why most businesses start with RAG

For the most common business need — making AI answer accurately from your own, current information — RAG is the pragmatic default:

  • Faster and cheaper than retraining a model.
  • Easy to update — change the underlying data anytime, and the AI reflects it.
  • Grounded — answering from your real sources reduces hallucination (see RAG explained for business leaders).

When fine-tuning is worth it

Fine-tuning suits consistent specialized behavior, tone, or output format that prompting and RAG can’t reliably achieve — a very specific writing style or task pattern, say. It’s more involved, and it doesn’t keep your data fresh by itself. So even when used, it’s often combined with RAG (fine-tune for behavior, RAG for current knowledge) rather than used alone.

The usual answer: RAG first

For most businesses, the path is RAG first, with fine-tuning added later only if a specific need justifies it. Starting with RAG gets you accurate, grounded AI quickly, and you can layer in fine-tuning if and when it earns its keep.

How dgm helps

dgm assesses your need in the $399 assessment and typically starts with RAG (grounding AI in your data), adding fine-tuning only where a specific behavior or style need justifies it — on a model-agnostic platform at $3,999/month. The choice serves your use case, not a default. If you’d rather explore the platform yourself first, go straight to osFoundry; if you want this decided and built right, that’s where dgm comes in.