RAG vs. Fine-Tuning: Which Is Right for Your AI Assistant?
Should you fine-tune a model or use RAG? For most business knowledge assistants, RAG wins on cost, freshness and accuracy. Here's why.
When teams want an AI assistant grounded in their own knowledge, two approaches come up: Retrieval-Augmented Generation (RAG) and fine-tuning. They solve different problems, and choosing well saves months of effort.
What RAG does
RAG retrieves relevant passages from your documents at query time and feeds them to the model, which then writes a grounded, cited answer. Your knowledge stays in a searchable index, so updates are instant and answers can cite their sources.
What fine-tuning does
Fine-tuning adjusts the model's weights on your data to change its style, format or behavior. It's powerful for teaching a consistent tone or specialized output format — but it does not reliably teach new facts, and updating knowledge means retraining.
The practical rule
For factual, frequently-changing knowledge — policies, docs, product data — start with RAG. Reach for fine-tuning when you need a specific behavior or format that prompting and retrieval can't achieve. Often the best systems combine both.
- Choose RAG for: knowledge that changes, citations, access control, lower cost.
- Choose fine-tuning for: consistent style, structured output, specialized tasks.