RAG vs Fine-Tuning: Choosing the Right AI Architecture

When a team wants an assistant that knows their business, the first instinct is often to train a custom model. In most cases retrieval-augmented generation (RAG) is the better starting point.

RAG keeps your knowledge in a searchable store and passes the relevant passages to the model at question time. Content stays current, answers can cite their sources, and access rules can be enforced per user.

Fine-tuning earns its place when you need a consistent style or format, or a narrow task performed at high volume. It does not reliably teach a model new facts, and every content change means another training run.

We usually start with RAG, measure answer quality against a set of real questions, and only reach for fine-tuning when the evaluation shows a gap retrieval cannot close.