AI
RAG vs Fine-Tuning: Choosing the Right Approach
Written by Facto IT Engineering TeamPublished 1 March 2026
Question
Should we fine-tune a model or use RAG for our enterprise AI use case?
Answer
For most business knowledge and operations copilots, start with RAG over approved sources. Fine-tune when you need durable style or task behavior that retrieval alone cannot provide — and only with clear evaluation and governance.
What RAG solves well
RAG grounds answers in documents you control — SOPs, policies, product notes — and makes citations possible. It is usually faster to ship, easier to update, and safer to audit than baking facts into model weights.
When fine-tuning helps
Fine-tuning can improve structured output formats, domain tone, or specialized classification. It does not replace access control, source of truth, or change management for business content that updates weekly.
Practical Facto IT guidance
We typically prototype RAG with evaluation sets, human-in-the-loop where needed, then consider light fine-tuning only after quality gaps are clear. Production designs include logging, source citations, and promotion controls.