Show HN: Subtle Failure Modes I Keep Seeing in Production‑Grade AI Systems

Show HN: Subtle Failure Modes I Keep Seeing in Production‑Grade AI Systems(github.com)

6 points by TXTOS 292 days ago | 2 comments

Hi HN,

Over the past two years I’ve built and debugged a fair number of production pipelines—mainly retrieval‑augmented generation stacks, agent frameworks, and multi‑step reasoning services. A pattern emerged: most incidents weren’t outright crashes, but silent structural faults that slowly compromised relevance, accuracy, or stability.

I began logging every recurring fault in a shared notebook. Colleagues started using the list for post‑mortems, so I turned it into a small public reference: 16 distinct failure modes (semantic drift after chunking, embedding/meaning mismatches, cross‑session memory gaps, recursion traps, etc.). The taxonomy isn’t academic; each item references a real outage or mis‑prediction we had to fix.

Why share it?

Common vocabulary – naming a failure mode makes root‑cause discussions faster and less hand‑wavy.

Earlier detection – several teams now check new features against the list before shipping.

Community feedback – if something is missing or misclassified, I’d rather learn it here than during another 3 a.m. incident.

The reference has already helped a few startups (and my own projects) avoid hours of trial‑and‑error. If you work on LLM infrastructure, you might find a familiar bug—or a new one to watch for. The link to the full table and brief write‑ups is in the “url” field of this Show HN post.

I’m not selling anything; it’s MIT‑licensed text. Comments, critiques, or additional failure patterns are very welcome.

Thanks for taking a look.

tgrrr9111 292 days ago |

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 292 days ago | |

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.