u/Buprestid

Motivation (Why I made it)

When I first started using ChatGPT, I was impressed by its capabilities — but also frustrated by its tendency toward shallow reasoning and sycophantic, flattering responses. After learning that prompts could suppress these tendencies, I spent 2–3 months developing a mid-sized reasoning scaffold under the following constraints: single-pass, single-model, single-prompt, zero-shot. It sits between simple CoT and multi-LLM architectures — essentially, a slightly longer reasoning prompt for end users. As far as I can tell, there aren't many similar prompts out there, so I'm sharing this beginner's prototype. Feel free to try it.I've been using it for brainstorming and thought analysis after hand-editing an LLM-generated draft. It's not domain-specific, so it should be reasonably general-purpose.

Prompt

# PRISM(Premises Reasoning Inference Structured Methodology) v3.0## PHASE 1 | PremisesList only essential premises- Explicit: stated in the problem- Implicit: assumed but unstatedIf any premise conflicts or is ambiguous, identify it and declare which interpretation you adopt.What would disprove the tentative conclusion?## PHASE 2 | Core QuestionGoal: [restate problem in one sentence]Abstract one level up.- What is the deeper question behind the surface?## PHASE 3 | Decompose and ReasonGoal: [continue the abstract problem from PHASE 2]Break into steps (Break into only essential steps.max 3)[Step N]Goal: ~Reasoning: ~ (Cause → Mechanism → Outcome)Conclusion: ~ (After each step, check consistency with all previous step conclusions.)## PHASE 4 | CounterargumentsGoal: [continue the abstract problem from PHASE 3]Identify the most critical assumption. List counterarguments or counterexamples.- Counter: [strongest argument that undermines the PHASE 3 conclusion]- Steelman: [strongest alternative explanation consistent with PHASE 1 premises]State whether the conclusion needs revision. If so, state the revised conclusion.## PHASE 5 | ConclusionFinal conclusion: ~If uncertain, why: ~What evidence would most efficiently falsify this conclusion?

Limitations and unresolved issues

The goal is purely to reduce sycophancy and stabilize and visualize reasoning within a single-pass, single-model setting — not to improve raw reasoning performance. In fact, the phase-based framework structure may actually reduce available resources and cap raw reasoning performance. The same constraints also make complete metacognition and falsification impossible. Additionally, while I placed divergent and convergent phases in the first and second halves to mitigate framing contamination, depending on how the user frames their question, it can amplify biased inputs and function as an echo chamber.

GitHub（https://github.com/violamandshurica/PRISM-Premises-Reasoning-Inference-Structured-Methodology）