> What you get is a beautiful animation that is 100% accurate and free of hallucinations.

I'm not sure I follow how this is actually guaranteed? The fact-checking process mentioned just seems to involve asking AI to review its own work.

All these LLM-as-review hype pieces don’t acknowledge that it’s turtles all the way down

Agreed. Given how many significant errors LLMs make in my topic of expertise, despite my taking multiple error checking steps, the idea of catching 100% of hallucinations because you told the LLM to check itself is hilarious. It’s just a wild lack of insight: “I’m using the LLM to teach me something I don’t know about, I definitely have the knowledge base to spot any errors that might remain!”

I don't do animations, but I have an answer. You research a topic well enough to be able to understand if the result is OK or not. Usually it means figuring out some sort of testing.

I'm researching causal inference right now, and my main goal was to make sure I understand how to test estimation on synthetic data.

Basically, it's the same way it works with people. If you delegate a task that you don't understand, and you can't have a credibility proof (i.e. doctors, lawyers), then you research a topic well enough to be able to (1) define the task and (2) verify the end result.

Completely agree. While I didn't set things up to have AI review its output in a loop, my experience trying to build a specific acoustic testing rig with Opus 5 also aligns with the other "it's turtles all the way down" comment.

Opus 5 first built me a detailed plan, but a couple important details were either obviously wrong or felt unnecessary. I went back and forth asking for sources and more information probably like 4 times and every time it did the "in looking at things in more detail it appears my previous advice was incorrect" spiel. It just became exhausting at some point because it feels like it really lays bare how LLMs are just minimizing that loss function but don't actually "understand" anything. It was really useful as a search engine (it correlated some highly relevant source docs), but I just couldn't trust it to believe it was actually done at any step.

even if you say use RAG or something to a source you can trust, there's no guarantee the agent will still use exactly what the source has.

i can't even get agents to remember core instructions like "use jq instead of writing a python script to parse some json"..

I could certainly envision a scenario whereby review would increase reliability but not how it would every guarantee 100%, there is a pretty big logical gap there.

You can add "make no mistakes" to the end of the prompt and achieve the same result while burning less tokens.

Yep, that's impossible. The hard truth is that most people this lost to LLM psychosis cannot understand that fact. It's better to treat it like someone in a cult, arguing the facts isn't going to help if they refuse to accept them.

I thought after reading the title that the text was about learning something, yet the actual text seems to be about having a system do something for me.