And that's how this will end up stagnating into nothing other than fractured enterprise "standards"

There is no evidence that (real AI) is even close to being solved, from a neuroscientific, algorithmic, computer science or engineering perspective. It's far more likely we're going down a dead-end path.

I'm now waiting for the rebrand when the ass falls out of AI investment, the same way it did when ML became passé.

so you are telling me that hallucinations (that by definition happen at the model layer) are an engineering problem ? so if we just spin up the right architecture, hallucinations won't be a problem anymore ? I have doubts

>so you are telling me that hallucinations (that by definition happen at the model layer) are an engineering problem ?

Yes. Hallucination is a problem from the days of using single shot prompting. No one is seriously doing that for anything anymore. You have an agentic refinement process with an evaluator in the loop using tool calls for verification and real time data.

>so you are telling me that hallucinations (that by definition happen at the model layer) are an engineering problem ?

Yes.

Hallucinations were a big problem with single shot prompting. No one is seriously doing that anymore. You have an agentic refinement process with an evaluator in the loop that takes in the initial output, quality checks it, and returns a pass/fail to close the loop or try again, using tool calls the whole time to inject verified/real time data into the context for decision making. Allows you to start actually building reliable/reasonable systems on top of LLMs with deterministic outputs.

okay give me the link to a LLM-based system that does not hallucinate then

LLMs can’t really evaluate things. They’re far too suggestible and can always be broken with the right prompt no matter how many layers you apply.