axezing121321

Why I kept running into “prompt spaghetti”—great model outputs but zero traceability. So I wrote a tiny spec that forces any LLM call to show its reasoning first.

What it looks like GOAL / CONTEXT / CONSTRAINTS ------------------------------ Premise 1 Premise 2 Rule applied Intermediate deduction Conclusion ------------------------------ SELF-CHECK → bias / loop / conflict flags

How to try 1. Download the release ZIP (link in post). 2. Copy `yaml_template.yaml`. 3. Paste it into ChatGPT (or any model) → you get an auditable logic tree.

Ask • Which failure modes am I missing? • Would you integrate something like this into CI / prod pipelines? • PRs with better examples or edge-cases are very welcome.

Thanks for looking!

Sorry, I think I replied in the wrong thread. My comment was meant for a different topic — feel free to ignore!

Sorry, I think I replied in the wrong thread. My comment was meant for a different topic — feel free to ignore!

Happy to elaborate on how ARC OS works — It parses subjective input into logic trees with assumptions, conflict checks, bias flags, and reasoning trails.

It’s symbolic only (no LLMs), designed for alignment auditing, law/policy frameworks, and decision explainability.

If anyone wants an example, I can post a breakdown here.