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.
axezing121321•7h ago
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!