Strategic Prompt Pricing for AIGC Services: A User-Centric Approach

• URL: http://arxiv.org/abs/2503.18168v1
• Date: Sun, 23 Mar 2025 18:41:06 GMT
• Title: Strategic Prompt Pricing for AIGC Services: A User-Centric Approach
• Authors: Xiang Li, Bing Luo, Jianwei Huang, Yuan Luo,
• Abstract summary: Current approaches overlook users' strategic two-step decision-making process in selecting and utilizing generative AI models.<n>We introduce prompt ambiguity, a theoretical framework that captures users' varying abilities in prompt engineering.<n>Our OPP algorithm achieves up to 31.72% improvement in platform payoff compared to existing pricing mechanisms.
• Score: 21.554792002413798
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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