GenAIOps for GenAI Model-Agility

• URL: http://arxiv.org/abs/2502.17440v1
• Date: Thu, 19 Dec 2024 03:29:03 GMT
• Title: GenAIOps for GenAI Model-Agility
• Authors: Ken Ueno, Makoto Kogo, Hiromi Kawatsu, Yohsuke Uchiumi, Michiaki Tatsubori,
• Abstract summary: We discuss so-called GenAI Model-agility, which we define as the readiness to be flexibly adapted to base foundation models as diverse as the model providers and versions.<n>First, for handling issues specific to generative AI, we first define a methodology of GenAI application development and operations, as GenAIOps, to identify the problem of application quality degradation caused by changes to the underlying foundation models.<n>We study prompt tuning technologies, which look promising to address this problem, and discuss their effectiveness and limitations through case studies using existing tools.
• Score: 2.7396907658239424
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI-agility, with which an organization can be quickly adapted to its business priorities, is desired even for the development and operations of generative AI (GenAI) applications. Especially in this paper, we discuss so-called GenAI Model-agility, which we define as the readiness to be flexibly adapted to base foundation models as diverse as the model providers and versions. First, for handling issues specific to generative AI, we first define a methodology of GenAI application development and operations, as GenAIOps, to identify the problem of application quality degradation caused by changes to the underlying foundation models. We study prompt tuning technologies, which look promising to address this problem, and discuss their effectiveness and limitations through case studies using existing tools.

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