"Should I fine-tune or use RAG?" is the wrong framing — they solve different problems. Building a RAG pipeline gives a model knowledge it can cite; fine-tuning changes how a model behaves. Picking by problem, not hype, saves months.
Use RAG for knowledge
If the need is "answer from our docs, our data, this week's changes," that's retrieval. RAG updates instantly (re-ingest a doc), cites its sources, and never bakes stale facts into weights. Anything that changes or must be attributable belongs in RAG.
Use fine-tuning for behavior
Fine-tuning is for form, not facts: a consistent tone, a strict output format, a domain style the base model won't hold. It's slow to update, can't cite anything, and will happily hallucinate facts you thought you trained in — so don't use it as a knowledge store.
Often you want both
A fine-tuned model for house style, fed grounded context by RAG, is a common production shape. Start with RAG — it's cheaper, faster to iterate, and easier to evaluate — and reach for fine-tuning only when behavior, not knowledge, is the gap. Kit for AI covers the RAG half end to end. Start free to build it.