针对大模型更新导致提示词失效问题，提出系统化迁移方案恢复应用稳定性。
Prompt Migration: Stabilizing GenAI Applications with Evolving Large Language Models
- 设计提示词重设计与测试平台，实现跨模型平稳迁移。
- 实测表明，迁移后应用可靠性完全恢复至初始水平。
- 适合需要长期稳定运行的生成式AI企业应用开发者。
生成式AI正通过自然语言接口和智能自动化重塑业务应用，但底层大语言模型（LLMs）的快速迭代使得提示词的一致性难以维持，导致应用行为不一致且不可预测，削弱了企业对关键工作流的可靠性要求。本文提出提示词迁移（Prompt Migration）概念，作为应对大模型演进的系统性方法。以企业级搜索应用Tursio为案例，分析连续GPT模型升级的影响，构建包含提示词重设计与迁移测试平台的框架，并验证其在恢复应用一致性方面的有效性。结果表明，结构化提示词迁移可完全挽回因模型漂移导致的应用可靠性损失。最后总结实践经验，强调提示词生命周期管理与稳健测试对确保生成式AI业务应用可靠性的必要性。
原文摘要 · Abstract (English)
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.
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