为提示词资产建立可量化的成熟度评估体系，提升生成式AI落地可靠性。
Prompt Readiness Levels (PRL): a maturity scale and scoring framework for production grade prompt assets
- 借鉴技术成熟度等级设计九级提示词成熟度标准
- 提出多维度评分机制，设置准入阈值防薄弱环节失效
- 适用于跨团队跨行业提示词工程的规范管理
提示工程已成为生成式AI系统中的关键生产环节。然而，组织仍缺乏统一、可审计的方法来评估提示资产是否满足运营目标、安全约束与合规要求。本文提出提示词成熟度等级（PRL），一个受技术成熟度等级（TRL）启发的九级成熟度量表，以及提示词成熟度评分（PRS），一种包含准入阈值的多维评分方法，旨在防止弱链路失效。PRL/PRS提供了一套原创的、结构化的方法论框架，用于规范提示资产的定义、测试、可追溯性、安全评估和部署准备，实现跨团队与跨行业的可复现资格判定，使提示工程具备可量化评估能力。
原文摘要 · Abstract (English)
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.
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