Prompt Migration: Stabilizing GenAI Applications with Evolving Large Language Models

TL;DR

This paper presents prompt migration as a systematic method to maintain stability and reliability in GenAI applications despite rapid evolution of large language models, demonstrated through a case study with GPT upgrades.

Contribution

It introduces a structured prompt migration framework including redesign and testing to stabilize GenAI applications amid LLM model updates.

Findings

Prompt migration restores application consistency after model upgrades.

Structured prompt redesign can recover lost reliability.

Practical lessons highlight importance of prompt lifecycle management.

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.