RAG or fine-tuning: how to give your data to an AI?

To get an AI to answer questions about your data, there are two approaches: RAG (the AI retrieves information from your documents at answer time) and fine-tuning (you retrain the model on your data). For most enterprise use cases, the right answer is RAG — here's why.

RAG vs fine-tuning: the difference in one sentence

• RAG: the model stays generic, but you give it on-the-fly access to your documents. It cites its sources.
• Fine-tuning: you modify the model itself so it adopts a tone, a format or a domain. It doesn't learn recent facts.

When to choose RAG

RAG is the answer whenever:

• your data changes (contracts, emails, stock, prices) — no retraining needed
• you want sourced, verifiable answers (essential in legal, finance, construction)
• you must segment access by role

Bonus: by grounding answers in real documents, RAG sharply reduces hallucinations.

When fine-tuning makes sense

Fine-tuning shines for form, not facts: enforcing a writing style, a strict output format, an industry jargon. But it costs more, freezes knowledge at training time, and can't explain its answers.

What about "long context" (1M tokens)?

Pasting everything into the prompt on every request looks simple… until the bill arrives: 20 to 24× more expensive per question than RAG, with answers that degrade past a certain volume. RAG only retrieves what's relevant.

In practice: often both

The best-performing systems combine RAG for facts (fresh, sourced) and light fine-tuning for style. You almost always start with RAG: faster to deploy, cheaper, scalable.

Where to start

You don't need to retrain a model to "teach it your company". Connect your sources with RAG first: you'll get 90% of the result in a fraction of the time and budget. Also read: Your AI knows the internet, not your company.