So RAG (retrieval augmented generation) is all the rage, but there are problems with it that make it appear almost conceptually flawed - to the point where resu

So RAG (retrieval augmented generation) is all the rage, but there are problems with it that make it appear almost conceptually flawed - to the point where results are flat-out poor? There‘s the paper about non-uniform attention („Lost in the Middle: How Language Models Use Long Contexts“) and some other paper mentioned that LLMs may de-focus on irrelevant retrieved content as soon as the ratio between relevant vs. irrelevant content becomes small. So, what‘s the current best practice to actually embed your content within the model?