The issue I had with RAG when I tried building our own internal chat/knowledge bot was pulling in the relevant knowledge before sending to the LLM. Domain questions like "What is Cat Block B?" are common and, for a human, provide all the context that is needed for someone to answer within our org. But vectorizing that and then finding matching knowledge produced so many false positives. I tried to circumvent that by adding custom weighting based on keywords, source (Confluence, Teams, Email), but it just seemed unreliable. This was probably a year ago and, admittedly, I was diving in head first without truly understanding RAG end to end.

Being able to just train a model on all of our domain knowledge would, I imagine, produce much better results.

I don't think RAG is dead, and I don't think NFTs have any use and think that they are completely dead.

But the OP's blog is more about ZK than about NFTs, and crypto is the only place funding work on ZK. It's kind of a devil's bargain, but I've taken crypto money to work on privacy preserving tech before and would again.

Not OP, but...

> Of course you would have to set a temperature of 0 to prevent abuse from the operator, and also assume that an operator has access to the pre-prompt

Doesn't the fact that LLM's are still non-deterministic with a 0 temperature render all of this moot? And why was I compelled to read a random blog post on the unsolved issue of validating natural language? It's a SQL injection except without a predetermined syntax to validate against, and thus a NP problem we've yet to solve.

I have no interest in anything crypto, but they are making a proposal about NFTs tied to AI (LLMs and verifiable machine learning) so they can make ownership decisions.

So it'd be alive in the making decisions sense, not in a "the technology is thriving" sense.

Wait, what does NFTs have to do with RAG?