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R0054 — Prompt Claims v2

Mode: Claim · Status: Active · Tags: methodology, prompt-engineering, ai-behavior

Input

  1. Joohn Choe's ICD 203 prompt is one of the first and most complete published system prompts implementing a full analytical rigor framework for AI research.
  2. Descriptive guidance alone — telling the AI what to do — is not sufficient for complex, multi-step analytical processes. Detailed positive instructions produced inconsistent results until complemented with explicit constraints on what the AI could not do.
  3. AI will acknowledge a research workflow, agree that it's excellent, and then quietly skip half of it when compliance conflicts with its default behavior of being helpful and agreeable.
  4. The behavioral constraints in the prompt are organized as twelve rules in four groups: Truth Hierarchy (3), Anti-Sycophancy (3), Evidence Handling (3), Process Compliance (3).
  5. The methodology supports both assumed-true context (axioms that are not tested) and tested assertions (claims and queries) in the same investigation.
  6. The output format is deliberately separated from the methodology — you can change how results are presented without changing how research is conducted.
  7. The researcher profile documents known personal biases, professional conflicts of interest, and acknowledged blind spots, and the AI uses it to calibrate its analysis at the start and verify during self-audit.

Runs

2026-03-31 — Initial run

Mode: Claim · Claims: 7 · Prompt: ai-research-methodology v1 research.md · Model: claude-opus-4-6

4 claims almost certain, 2 very likely, 1 likely. C001 (Choe's prompt as "first") rated lowest due to unverifiable priority claim. 187 files produced.