The short answer
Most companies should start with RAG and add fine-tuning only in specific cases. The reason: the most common need is "answer based on our own, changing data" — which is exactly what RAG is good at — while fine-tuning is good at "a fixed style, format, or narrow repetitive task."
Compare on four axes
- Cost: RAG is mostly retrieval and vector-store running cost; changing content needs no retraining. Fine-tuning has training cost and must be redone whenever data changes.
- Maintenance: updating RAG knowledge = updating the document store, effective in minutes; updating a fine-tune = retraining and re-validation.
- Data security: RAG can keep sensitive data in a private retrieval layer, require source citations and apply permission filtering; fine-tuning bakes data into the weights, which is harder to remove or audit.
- Update frequency: choose RAG when knowledge changes often; fine-tuning when it barely changes and you want a stable output style.
When you actually need fine-tuning
Fine-tuning pays off when you want fixed, predictable output — a specific summary format, a specific support tone, or compressing a long prompt into the model to cut latency and token cost. It isn't mutually exclusive with RAG: a common mature pattern is "RAG supplies facts, fine-tuning controls style."
A simple decision table
- Knowledge changes, needs traceable sources, data is sensitive → RAG
- Fixed style/format, narrow repetitive task, latency-sensitive → Fine-tuning
- Both → RAG + light fine-tuning
In practice we almost always start with RAG — get the system answering correctly from company data with traceable sources first — then decide whether fine-tuning is worth adding. Get it right first, then fast and cheap.