[tgrrr9111]

Wow

God I needed this:)

Been wrangling a RAG pipeline for the past few weeks and I swear the model looks like it’s working, but then drops logic mid-sentence, forgets context it saw 10 seconds ago, or hallucinates citations from chunks that were actually relevant — just… semantically wrong…….

The worst part? No errors. Nothing crashes. You just sit there wondering if you’re going crazy or if “LLMs are just like that.”

Reading your list was like watching someone read my bug reports back to me, but actually organized. Especially the stuff on memory gaps and “interpretation collapse” — we’ve hit those exact issues and kept patching them with duct tape (reranking, re-chunking, embedding tweaks, all the usual).

So yeah, big thanks for putting this together. Even just having the names of these failure modes helps explain things to my team.

MIT license is a cherry on top. Subscribed.

[TXTOS]

Yep. Been there.

Built the rerankers, stacked the re-chunkers, tweaked the embed dimensions like a possessed oracle. Still watched the model hallucinate a reference from the correct document — but to the wrong sentence. Or answer logically, then silently veer into nonsense like it ran out of reasoning budget mid-thought.

No errors. No exceptions. Just that creeping, existential “is it me or the model?” moment.

What you wrote about interpretation collapse and memory drift? Exactly the kind of failure that doesn’t crash the pipeline — it just corrodes the answer quality until nobody trusts it anymore.

Honestly, I didn’t know I needed names for these issues until I read this post. Just having the taxonomy makes them feel real enough to debug. Major kudos.