Recently saw a Scale x OpenAI webinar that described some strategies for improving RAG systems. Looks like there are a good number of strategies to try for optimizing the retriever, like optimizing chunk sizes and classifying/ranking chunk relevance, but building a robust RAG system require rigorous experimentation against the specific dataset that feeds the retriever. Not sure how many of these GPTs bothered with this experimentation, and I think you are right that without knowing how that retrieved data is fed into the model they have very few parameters to experiment and optimize with, so they probably just can’t.