> increases accuracy and reliability of an LLM while also making the whole system harder to understand Harder than what? If the alternative is a black box fine

> increases accuracy and reliability of an LLM while also making the whole system harder to understand Harder than what? If the alternative is a black box fine tuned model, RAG is obviously superior. There’s no easy way to audit why an LLM spits out nonsense, and the only way we know how to make it less nonsensical is to feed reasonable inputs. So RAG replaces a poorly understood problem (zero-shot generation from decoder-only models) with a well-understood one and a poorly understood one (search retrieval followed by zero shot generation from documents). I think it’s a little disingenuous for all of these out of the box repos to tout their GitHub stars and claim to be “leading” in RAG. The first step is retrieval. People have been working on retrieval for decades and vector-based retrieval since before attention was all we needed. That said, I’m glad that the prospect of text generation has forced people to become familiar with information retrieval. It’s a relatively new subject compared to the rest of CS and I really only learned about in industry.