为AI生成内容平台设计用户导向的提示词定价策略，提升平台收益。
Strategic Prompt Pricing for AIGC Services: A User-Centric Approach
- 引入提示模糊度概念，量化用户提示能力与生成效果关系。
- 提出的最优提示定价算法使平台收益最高提升31.72%。
- 适合关注AIGC商业变现与用户行为建模的研究者。
AI生成内容（AIGC）服务的快速发展催生了高效提示词定价策略的迫切需求，但现有方法忽视了用户在选择和使用生成式AI模型时的战略性两步决策过程。这一疏漏带来两个关键挑战：量化用户提示能力与生成结果之间的关系，以及在考虑用户行为异质性的前提下优化平台收益。本文提出提示模糊度理论框架，捕捉用户在提示工程上的能力差异，并构建最优提示定价（OPP）算法。分析揭示出反直觉现象：提示模糊度较高（即能力较低）的用户其提示使用量随模糊度先增后减，反映边际效用的复杂变化。基于字符级GPT类模型的实验表明，相较于现有定价机制，本算法可使平台收益提升高达31.72%，验证了用户导向提示定价在AIGC服务中的重要性。
原文摘要 · Abstract (English)
The rapid growth of AI-generated content (AIGC) services has created an urgent need for effective prompt pricing strategies, yet current approaches overlook users' strategic two-step decision-making process in selecting and utilizing generative AI models. This oversight creates two key technical challenges: quantifying the relationship between user prompt capabilities and generation outcomes, and optimizing platform payoff while accounting for heterogeneous user behaviors. We address these challenges by introducing prompt ambiguity, a theoretical framework that captures users' varying abilities in prompt engineering, and developing an Optimal Prompt Pricing (OPP) algorithm. Our analysis reveals a counterintuitive insight: users with higher prompt ambiguity (i.e., lower capability) exhibit non-monotonic prompt usage patterns, first increasing then decreasing with ambiguity levels, reflecting complex changes in marginal utility. Experimental evaluation using a character-level GPT-like model demonstrates that our OPP algorithm achieves up to 31.72% improvement in platform payoff compared to existing pricing mechanisms, validating the importance of user-centric prompt pricing in AIGC services.
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