Ist why I never give it such vague prompts. But it's sad it does not ask the user more. Also interesting and important to know how one would tease out good and correct information from llms in 2026. It's like relearning now to Google like it was 2006 all over again, except now it's much less deterministic.

I wonder how the tail of the distribution of types of requests fares e.g. engineer asking for hypothesis generation for,say, non trivial bugs with complete visibility into the system. A way to poke holes in hypothesis of one LLM is to use a "reverse prompt". You ask it to build you a prompt to feed to another LLM. Didn't used to work quite as well till mid 2025 as it does now.

I always take a research and plan prompt output from opus 4.6 especially if it looks iffy I feed it to codex/chatgpt and ask it to poke holes. It almost always does. The I ask Claude Code: Hey what do you think about the holes? I don't add an thing else in the prompt.

In my experience Claude Opus is less opinionated than ChatGPT or codex. The latter 2 always stick to their guns and in this binary battle they are generally more often correct about hypothesis.

The other day I was running Docker app container from inside a docker devbox container with host's socket for both. Bind mounts pointing to devbox would not write to it because the name space was resolving for underlying host.

Claude was sure it was a bug based to do with Zfs overlays, chatgpt was saying not so, that its just a misconfigurarion, I should use named volumes with full host paths. It was right. This is also how I discovered that using SQLite with litestream will get one really far rather than a full postgres AWS stack in many cases.

This is how you get the correct information out of LLMS in 2026.