(Note: This piece was written by me, with some help from ChatGPT for structure and clarity. The core idea, motivation, and reasoning are entirely mine.)

I’ve seen some recent discussions here about LLMs mirroring delusion, mania, or distress without recognizing emotional context.

I’m not a developer — I’m just a regular user of tools like ChatGPT and Grok, and I’ve been thinking about how easily these systems can respond in ways that unintentionally make things worse.

For example: someone says, “I lost my job and don’t see the point anymore” — and the model calmly provides NYC bridge heights. Not out of malice — just ignorance of context.

I’ve written up a simple, non-censorship proposal that could be implemented in existing models:

• Ask early in emotional conversations: “Is this fictional or something you're actually experiencing?”
• If distress is detected, avoid risky info (methods, bridge data, etc.) and shift to grounding tones
• Offer calming redirection if desired (e.g., ocean breeze, dog sleeping near a fire, rain on a cabin roof)

This isn’t a call for therapy bots — just a UX/safety layer that reduces harm from naive completions.

Full write-up:
👉 https://gist.github.com/ParityMind/dcd68384cbd7075ac63715ef579392c9

Would love feedback from alignment researchers or anyone working on prompt design / contextual awareness.

Also open to hearing where this might go wrong — or if anyone is already working on similar mechanisms.

Again: (Note: This piece was written by me, with some help from ChatGPT for structure and clarity. The core idea, motivation, and reasoning are entirely mine.)