RAG vs Fine-Tuning: What You Actually Need to Ship an AI Product

Fine-tuning sounds like the 'serious' option, so it's often the default reach — but it's solving a narrower problem than most teams think. It changes how a model behaves or writes; it doesn't reliably teach it new facts, and it needs to be redone every time your underlying information changes.

Retrieval-augmented generation solves the far more common problem: 'answer questions using our documents, correctly, with citations, and stay current when those documents change.' You update a vector index instead of retraining a model, which is both cheaper and faster to iterate on.

The honest rule of thumb: reach for RAG first for anything knowledge-based. Reach for fine-tuning only when you need a specific tone, format or behaviour that prompting and retrieval genuinely can't get you — and even then, often only after RAG alone proves insufficient.

This exact decision — and how to evaluate it rather than guess — is a full module in our Generative AI course, built around a retrieval assistant students ship themselves.