GenAIOps for GenAI Model-Agility
December 19, 2024 Β· Declared Dead Β· π arXiv.org
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Authors
Ken Ueno, Makoto Kogo, Hiromi Kawatsu, Yohsuke Uchiumi, Michiaki Tatsubori
arXiv ID
2502.17440
Category
cs.SE: Software Engineering
Cross-listed
cs.LG
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
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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