[moomoo11]

What sort of data are you working with?

In my case, users would be searching through either custom defined data models (I have custom forms and stuff), or if they were trying to find a comment on a Task, or various other attached data on common entities.

For example, "When did Mark say that the field team ran into an issue with the groundwater swelling?"

That would return the Comment, tied to the Task.

In my system there are discussions and comments (common) tied to every data entity (and I'm using graphdb, which makes things exponentially simpler). I index all of these anyway for OS, so the AI is able to construct the query to find this. So I can go from comment -> top level entity or vice versa.

I spent maybe 60-100 hours writing dozens maybe a hundred tests to get the prompt right, taking it from 95% success to 100% success. In over 2 months it hasn't failed yet.

Sorry, I should mention maybe our use-cases are different. I am basically building an audit log.

[TXTOS]

ah yeah that makes sense — sounds like you're indexing for traceability first, which honestly makes your graph setup way more stable than most RAG stacks I’ve seen.

I’m more on the side of: “why is this even the logic path the system thinks makes sense for the user’s intent?” — like, how did we get from prompt to retrieval to that hallucination?

So I stopped treating retrieval as the answer. It’s just an echo. I started routing logic first — like a pre-retrieval dialectic, if you will. No index can help you if the question shouldn’t even be a question yet.

Your setup sounds tight though — we’re just solving different headaches. I’m more in the “why did the LLM go crazy” clinic. You’re in the “make the query land” ward.

Either way, I love that you built a graph audit log that hasn’t failed in two months. That's probably more production-ready than 90% of what people call “RAG solutions” now.

[moomoo11]

Thanks :)

And really cool stuff you’re doing too. Honestly I have not spent as much time as maybe I should really diving into all the LLM tooling and stuff like you have.

Good luck!!

[TXTOS]

hey — really appreciate that. honestly I’m still duct-taping this whole system together half the time, but glad it’s useful enough to sound like “tooling”

I think the whole LLM space is still missing a core idea: that logic routing before retrieval might be more important than retrieval itself. when the LLM “hallucinates,” it’s not always because it lacked facts — sometimes it just followed a bad question.

but yeah — if any part of this helps or sparks new stuff, then we’re already winning. appreciate the good vibes, and good luck on your own build too