When teams come to us with "we want to use AI on our docs," the first question is almost always: RAG or fine-tuning? The honest answer: most of the time, you should start with RAG. Here is why, and when to break the rule.
TL;DR
- Start with RAG. It's faster, cheaper to update, and easier to debug.
- Fine-tune when you need consistent style, structured output, or domain reasoning that prompting alone can't reach.
- The real winner is usually both: RAG for grounded knowledge, light fine-tuning for tone and format.
What each one actually does
Retrieval-Augmented Generation keeps your data outside the model. At query time, you fetch the most relevant chunks and stuff them into the prompt. The model "knows" your data because you're handing it a fresh copy each time.
Fine-tuning bakes patterns into the model's weights. You are not adding facts so much as adjusting how the model generates: its style, its bias toward certain formats, its way of reasoning through your domain.
That distinction is everything. RAG changes what the model sees. Fine-tuning changes how the model thinks.
When RAG is the right call
- Your knowledge changes (docs, products, contracts)
- You need verifiable citations
- You can't risk leaking training data
- You want to ship next week, not next quarter
When fine-tuning earns its keep
- Output must follow a strict schema or tone every time
- Your domain has reasoning patterns that don't transfer (e.g., legal citation chains, medical differentials)
- You're hitting a context-length ceiling with RAG and the chunks aren't enough
- You need a smaller, cheaper model to imitate a larger one ("distillation")
A practical decision rule
If you can solve it with prompting and retrieval, do that first. Spin up RAG, ship it, measure where it fails. Then decide whether the failure modes call for fine-tuning. You'll discover that 80% of the time, better retrieval beats a fine-tune.
Don't skip the evals
Whichever path you pick, build evaluation in on day one. A golden dataset of 50 question and answer pairs that you actually trust will save you from arguing about quality based on vibes. Every model change, every prompt tweak, and every retrieval improvement gets a score before it ships.
That's what we mean when we say evaluation-first culture. It's the difference between an AI system you can iterate on and one you have to rebuild every six months.