IMPRD: Iterative Multi-Perspective Rhetorical Debugging for LLM-Assisted Content Optimization

Systematic methodologies for optimizing LLM-assisted content remain underdeveloped despite widespread adoption. We introduce IMPRD (Iterative Multi-Perspective Rhetorical Debugging), a cognitive scaffolding methodology that achieves consistent convergence from draft-quality (7.1-7.8) to publication-ready (8.5-9.0) content through structured multi-persona evaluation. IMPRD employs random odd-number sampling from persona pools (solving both tiebreaking and local minima problems), weighted scoring, and explicit convergence criteria. We demonstrate effectiveness across three orders of magnitude in content length—from social media posts (100 words, 1-2 iterations) to blog articles (3,000 words, 3-4 iterations) to book manuscripts (100,000 words, 60+ iterations)—with mean improvement of 1.3 points across all applications (n=28 content pieces). IMPRD extends IMPCD (Iterative Multi-Perspective Conceptual Debugging), which was developed through methodological bootstrapping: recursive self-application until convergence validated the multi-perspective iteration pattern. This bootstrapping approach provides a template for systematic methodology development. Our results suggest that external cognitive scaffolding through systematic methodology can extend less expensive model capabilities to approach more capable reasoning-focused models, and we propose REASON as a broader framework for developing cognitive scaffolding methodologies that externalize different reasoning patterns for LLM usage.