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Silent LLM Regressions
LLM model and prompt changes silently degrade output quality, format, latency, and behavior without detection until users are affected.
In their words
is there a clean way to tell adversarial evaluators "for this scenario class, the right answer is to refuse, score accordingly, no helpfulness penalty"?
Hazing clear guidelines and rules set out in memory files with instructions to always follow them to Claude just straight up ignoring instructions
I don't want any software in my engineering flow changing without me doing it on purpose
I'm trying to build a product that uses Claude models though, and for that I really need to be sure I'm getting the same model every time.
I can repeat the same question over and over and after each verification it is instructed to tell me it verified and it lies.
something that watches the outputs continuously and tells me "hey these 15 products the model recommended today don't actually exist in inventory"
we still have almost no empirical evaluation of what we're producing with these tools
The customer was obviously just running our messages through some LLM and copy and pasting the answers.
we had zero way of knowing it was happening. no logging, no alerting, nothing.
Every time I try to use models I eventually go back to using ChatGPT.
I get daily emails from clients running our decisions through LLMs and asking for ridiculous stuff
Has anyone benchmarked acceptance rate of valid tool calls across quant levels on one model? Not perplexity. Just did the JSON parse.
someone without reading it generate a feedback with llm with so much ambiguity that I have to get back and wait couple of more days
a tool call that returns 200 with a silently corrupted value... bills as a clean success and the retry logic never fires.
LLM app teams
Where they talk
- r/LLMDevs
- r/AI_Agents
- r/LocalLLM
- r/ClaudeAI
- r/mlops
- r/AIQuality
- r/ClaudeCode
- r/LocalLLaMA
Tools they use today
- Claude
- ChatGPT
- Claude Code
- LangSmith
- Braintrust
- DeepSeek
- Gemini
- Codex
Why those fall short
- model ignores instructions, guesses instead of reading specs, gets worse on bigger projects
- style instructions get ignored once the chat gets deep
- no empirical evaluation of LLM outputs; org does not understand LLM lifecycle
- models hallucinate, refuse, provide broken links, over-elaborate
- no built-in quality assurance; outputs look convincing but contain hidden errors
Map of ideas
Drawing the map…