Prompt Readiness Levels (PRL): a maturity scale and scoring framework for production grade prompt assets

TL;DR

This paper introduces a structured nine-level maturity scale and scoring framework for evaluating prompt assets in generative AI, aiming to improve safety, compliance, and operational readiness.

Contribution

It proposes the Prompt Readiness Levels (PRL) and Prompt Readiness Score (PRS), providing a novel, standardized framework for prompt asset qualification and governance.

Findings

PRL offers a nine-level maturity scale inspired by TRL.

PRS provides a multidimensional scoring method with gating thresholds.

Framework supports reproducible prompt qualification across industries.

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.