What is the difference between RAG and fine-tuning? When do you use each?

RAG injects knowledge at inference time — cheaper, instantly updatable, and the answer is traceable to a source. Fine-tuning bakes knowledge into model weights — better for consistent tone/format, faster inference, but expensive and static. Use RAG for frequently-changing or proprietary knowledge; fine-tune when a consistent style/task format matters more.