Every week a client asks: “Should we fine-tune on our data?” Almost always, the answer is no, not because fine-tuning is weak, but because RAG solves their actual problem faster, cheaper, and with less maintenance.
What RAG is for
RAG retrieves relevant chunks from your documents at query time and injects them into the model context. Best when knowledge changes often, you need citations, or you lack labelled training sets.
What fine-tuning is for
Fine-tuning adapts model behaviour, format, tone, domain patterns, into the weights. Best when you need consistent structure, low latency, or proprietary style that prompting cannot lock down.
Decision shortcuts: data changes weekly → RAG. Need citations → RAG. Need identical output shape every time → fine-tuning. Under 1,000 labelled examples → start with RAG.
The combination that wins
Our highest-performing systems often use both: fine-tuning for format and terminology, retrieval for factual grounding. Cost is higher. For enterprise accuracy, it is frequently the right architecture.