This document presents a structured methodology for iteratively refining AI-generated outputs to align with a reference standard while minimizing deviation. The approach integrates quantitative deviation measurement, targeted refinement techniques, and iterative convergence to enhance consistency across multiple AI-generated outputs. This framework applies to image generation, text-based AI outputs, procedural content generation, and other AI-assisted creative or computational tasks.
To assess the difference between an AI-generated output and a reference standard, a structured deviation scoring method is used. Each output is evaluated across multiple categories, with a weighted deviation score assigned based on observed variance.