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!

Here’s a full example from ARC Builder:

GOAL: Should we adopt 4-day work weeks? • Premises: • Burnout reduces long-term productivity • Productivity is currently stable but at risk • No legal constraints to switching • Constraints: Deliverables must not drop • Rule Applied: Cost-benefit logic (wellbeing vs output risk) • Bias Check: Potential optimism bias flagged • Confidence Level: Medium • Contrast Case: Reverses conclusion if burnout removed

The output includes conflict checks, rule reference, and audit trail. I’m curious how others here feel about symbolic scaffolds like this for alignment or decision evaluation?

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.