提出GenAIOps框架，解决大模型迭代导致应用质量下降的问题
GenAIOps for GenAI Model-Agility
- 构建GenAIOps方法论，应对生成式AI模型更新带来的应用风险
- 通过案例研究验证提示调优在模型迁移中的有效性与局限性
- 适合关注GenAI应用稳定性的研发与运维团队参考
AI敏捷性使组织能快速响应业务需求，尤其适用于生成式AI（GenAI）应用的开发与运维。本文聚焦于GenAI模型敏捷性，即灵活适配不同模型提供商及版本的能力。针对生成式AI特有的问题，提出GenAIOps方法论，识别基础模型变更引发的应用质量下降问题。通过案例研究分析提示调优技术的有效性与局限性，评估现有工具的实际表现。
原文摘要 · Abstract (English)
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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