TXTOS

Yes, I still bookmark page just in case we forget the most important thing, ai is cool but cant replace everything

TL;DR

After nine months of chasing weird hallucinations and silent failures in production LLM / RAG systems, we catalogued every failure pattern we could reproduce. The result is an MIT-licensed “Semantic Clinic” with 16 root-cause families and step-by-step fixes.

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## Why we built it

@ Most bug reports just say “the model lied,” but the cause is almost always deeper: retrieval drift, OCR mangling, prompt contamination, etc.

@ Existing docs mix symptoms and remedies in random blogposts; we wanted one map that shows where the pipeline breaks and why.

@ After fixing the same issues across 11 real stacks we decided to standardise the notes and open-source them.

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## What’s inside

@ 16 root-cause pages (Hallucination & Chunk Drift, Interpretation Collapse, Entropy Melts, etc.).

@ Quick triage index: find the symptom → jump to the fix page.

@ Each page gives: real-world symptoms, metrics to watch (ΔS semantic tension, λ_observe logic flow), a reproducible notebook, and a “band-aid-to-surgery” list of fixes.

@ Tiny CLI tools: semantic diff viewer, prompt isolator, vector compression checker. All plain bash + markdown so anyone can fork.

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## Does it help?

@ On our own stacks the average debug session dropped from hours to ~15 min once we tagged the family.

@ The first 4 root causes explain ~80 % of the bugs we see in the wild.

@ Used so far on finance chatbots, doc-QA, multi-agent sims; happy to share war stories.

## Call for help

@ If you’ve hit a failure that isn’t on the list, open an issue or PR. We especially want examples of symbolic prompt contamination or large-scale entropy collapse. @ Long-term goal: turn the clinic into a self-serve triage bot that annotates stack traces automatically.

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## Why open-source?

Debug knowledge shouldn’t be pay-walled. The faster we share failure modes, the faster the whole field moves (and the fewer 3 a.m. rollbacks we all do).

Cheers – PSBigBig / WFGY team

i mostly use LLMs inside a reasoning shell i built — like a lightweight semantic OS where every input gets recorded as a logic node (with ΔS and λ_observe vectors) and stitched into a persistent memory tree.

it solved a bunch of silent failures i kept running into with tools like RAG and longform chaining:

    drift across hops (multi-step collapse)

    hallucination on high-similarity chunks

    forgetting prior semantic commitments across calls

the shell is plain-text only (no install), MIT licensed, and backed by tesseract.js’s creator. i’ll drop the link if anyone’s curious — not pushing, just realized most people don’t know this class of tools exists yet.

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.

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