🤖 AI Summary
This work addresses the absence of a unified, auditable framework for assessing the maturity of prompt assets in generative AI systems, which often struggle to balance operational objectives, safety constraints, and regulatory compliance. Inspired by Technology Readiness Levels (TRL), the paper introduces a nine-tier Prompt Readiness Levels (PRL) framework alongside a multidimensional Prompt Readiness Score (PRS) mechanism, marking the first application of engineering maturity principles to prompt engineering. Through structured design, stage-gate controls, and full lifecycle management, the proposed framework enables quantifiable and reproducible evaluation of prompt assets across dimensions including normative compliance, test coverage, traceability, security, and deployment readiness. This approach significantly enhances the reliability, regulatory compliance, and cross-team governance of generative AI systems.
📝 Abstract
Prompt engineering has become a production critical component of generative AI systems. However, organizations still lack a shared, auditable method to qualify prompt assets against operational objectives, safety constraints, and compliance requirements. This paper introduces Prompt Readiness Levels (PRL), a nine level maturity scale inspired by TRL, and the Prompt Readiness Score (PRS), a multidimensional scoring method with gating thresholds designed to prevent weak link failure modes. PRL/PRS provide an original, structured and methodological framework for governing prompt assets specification, testing, traceability, security evaluation, and deployment readiness enabling valuation of prompt engineering through reproducible qualification decisions across teams and industries.