Why is Retrieval Augmented Generation (RAG) not everywhere?

I’m relatively new to the world of large languages models and I’m currently hiking up the learning curve.

RAG is a seemingly cheap way of customising LLMs to query and generate from specified document bases. Essentially, semantically-relevant documents are retrieved via vector similarity and then injected into an LLM prompt (in-context learning). You can basically talk to your own documents without fine tuning models. See here: https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-foundation-models-customize-rag.html

This is exactly what many businesses want. Frameworks for RAG do exist on …

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