GenAIOps for GenAI Model-Agility
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 25 references that are in the map. First posted on arXiv 19 Dec 2024.
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References · 9
- 2016Equality of Opportunity in Supervised Learningcited by 2,007
- 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,045
- 2023Large Language Models as Optimizerscited by 864
- 2023Automatic Prompt Optimization with "Gradient Descent" and Beam Searchcited by 653
- 2017On Fairness and Calibrationcited by 406
- 2023EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizerscited by 193
- 2020Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodologycited by 41
- 2021Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?cited by 37
- 2024Symbolic Prompt Program Search: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimizationcited by 30
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