RAG vs Fine-Tuning — I've Used Both in Production, Here's What Actually Matters

Every AI team hits this fork in the road: do we bolt on RAG, or fine-tune the model? I've shipped both approaches in production systems, and the "right answer" is less about technology and about what problem you're actually solving. The Core Difference in 30 Seconds RAG (Retrieval-Augmented Generation) keeps your base model untouched. At query time, you fetch relevant documents from a vector and stuff them into the prompt. The model reads your data like a student reading notes during an open-book exam. Fine-tuning changes the model's weights.