GenAIOps for GenAI Model-Agility

TL;DR

This paper explores GenAIOps, a methodology for enhancing the agility of generative AI applications by managing model changes, and evaluates prompt tuning techniques for maintaining application quality amidst foundation model updates.

Contribution

It introduces the concept of GenAI Model-agility and proposes a methodology for managing model changes, including an analysis of prompt tuning effectiveness and limitations.

Findings

Prompt tuning can mitigate quality degradation due to model updates.

Prompt tuning effectiveness varies across different tools and scenarios.

Identifies limitations of current prompt tuning approaches.

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.