RAG, or retrieval augmented generation, is probably the most in-demand pattern in AI engineering right now. It's been around a few years and it's still wildly popular, and a huge percentage of the real AI apps you use have some form of it.
The concept is simple: the model was trained on general data, but your user wants answers about their data, and RAG closes that gap. You take that data and store it in something like a vector database. Then when someone asks a question, you search the database, find the most relevant results, and feed them into the model so it can reason over them.
Now the model has both its original training data and the specific context from your user. It's one of the most useful things large language models can do, and it's harder than it looks once you're doing it at scale.
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