Fine-tuning vs RAG
Two different ways to specialize a model for your use case add knowledge at training time vs at query time.
RAG injects fresh, relevant documents into the model's context at query time. Fine-tuning bakes new behavior into the model's weights at training time. They solve different problems and are often combined.
Use RAG when: knowledge changes often, you need citations, you have lots of documents, and you can tolerate retrieval latency.
Use fine-tuning when: you need a specific output style or format, the task is narrow, or you want to make a smaller model behave like a larger one for cost reasons.
The rule of thumb in 2026: try good prompts first, then RAG, then fine-tune only if neither solves the problem.