When Should You Use RAG vs Fine-Tuning in Microsoft Foundry?

In simple terms, both RAG and fine-tuning are used to improve how an AI model gives answers, but they are used in different situations. RAG (Retrieval-Augmented Generation) is useful when you want the model to use external or frequently updated data. Instead of changing the model itself, it retrieves information from documents or a database and then generates the answer. This is helpful for things like company knowledge bases or documentation where information changes often. Fine-tuning is better when you want the model to learn a specific style, behavior, or domain knowledge. You train the model on your own dataset so it becomes better at certain tasks. In short, use RAG when you need updated information, and use fine-tuning when you want to change how the model behaves or responds.