Prompt Migration: Stabilizing GenAI Applications with Evolving Large Language Models

2025-07-07

AI Summary

Rapid iteration of large language models (LLMs) causes prompt degradation, behavioral drift, and performance deterioration in generative AI (GenAI) applications. Method: This paper proposes the first systematic prompt migration framework to ensure stability and reliability of enterprise-grade GenAI applications amid LLM evolution. The framework integrates prompt refactoring mechanisms with an extensible migration testbed, unifying prompt engineering, automated regression testing, and lifecycle management strategies. It is empirically validated on Tursio, an enterprise search system. Results/Contribution: The approach fully restores performance degraded by model updates, significantly improving application consistency and maintainability. Its core contribution is the formalization of “prompt migration” as a novel concept and the construction of an end-to-end solution, establishing a new paradigm for resilient, lifecycle-aware evolution of GenAI systems.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Generative AI is transforming business applications by enabling natural language interfaces and intelligent automation. However, the underlying large language models (LLMs) are evolving rapidly and so prompting them consistently is a challenge. This leads to inconsistent and unpredictable application behavior, undermining the reliability that businesses require for mission-critical workflows. In this paper, we introduce the concept of prompt migration as a systematic approach to stabilizing GenAI applications amid changing LLMs. Using the Tursio enterprise search application as a case study, we analyze the impact of successive GPT model upgrades, detail our migration framework including prompt redesign and a migration testbed, and demonstrate how these techniques restore application consistency. Our results show that structured prompt migration can fully recover the application reliability that was lost due to model drift. We conclude with practical lessons learned, emphasizing the need for prompt lifecycle management and robust testing to ensure dependable GenAI-powered business applications.

Problem

• Stabilizing GenAI applications with evolving large language models
• Addressing inconsistent behavior due to rapid LLM changes
• Ensuring reliability in mission-critical business workflows

Innovation

• Prompt migration stabilizes GenAI with changing LLMs
• Framework includes prompt redesign and migration testbed
• Structured migration recovers lost application reliability