Today's concept: Rag vs fine-tuning, the misconceptions

Every few weeks someone asks me whether they should use RAG or fine-tuning. The question sounds like a fork in the road.

It isn't one, and treating it as one is what makes these projects expensive.

Plain definitions first.

RAG (retrieval augmented generation) means that before the model answers, you search your own documents and paste the relevant bits into the prompt. The model learns nothing. It reads what you handed it, the way you'd skim a page before answering a question.

Fine-tuning means you train an existing model further on your own examples, so its weights shift. It reads nothing extra at answer time. It just behaves differently by default.

Three myths worth killing.

One: fine-tuning is how you teach a model your company's facts. It isn't. Training on documents shapes how a model talks far more reliably than what it recalls, so the fact you needed comes back as a confident guess. Retrieval is what makes a fact checkable, because you can point at the source it came from.

Two: big context windows made retrieval obsolete. Pasting the whole handbook into every request bills you for the whole handbook on every request, and burying one relevant paragraph among a hundred irrelevant ones tends to make the answer worse, not better.

Three: you pick one. Most systems that work in production use both. Retrieval for what changes. Fine-tuning for how the thing should sound and decide.

The part I didn't expect: once you run both, most arguments about which to use turn out to be retrieval quality problems in a costume. If the search step hands over the wrong three paragraphs, no amount of training rescues the answer.

So measure the retrieval step on its own, before you judge the model at all.

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#Rag #AI #LLM #AIAgents #MachineLearning