Prompt Readiness Levels (PRL): a maturity scale and scoring framework for production grade prompt assets
This addresses the problem of managing prompt assets for organizations using generative AI, though it is incremental as it adapts existing maturity scale concepts to a new domain.
The paper tackles the lack of a shared, auditable method for qualifying prompt assets in generative AI systems by introducing Prompt Readiness Levels (PRL) and Prompt Readiness Score (PRS), providing a structured framework for governance, testing, and deployment readiness.
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.