Prompt Migration: Stabilizing GenAI Applications with Evolving Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908440383717376 |
|---|---|
| author | Tripathi, Shivani Nema, Pushpanjali Halder, Aditya Qiao, Shi Jindal, Alekh |
| author_facet | Tripathi, Shivani Nema, Pushpanjali Halder, Aditya Qiao, Shi Jindal, Alekh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05573 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prompt Migration: Stabilizing GenAI Applications with Evolving Large Language Models Tripathi, Shivani Nema, Pushpanjali Halder, Aditya Qiao, Shi Jindal, Alekh Databases Artificial Intelligence Software Engineering 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. |
| title | Prompt Migration: Stabilizing GenAI Applications with Evolving Large Language Models |
| topic | Databases Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2507.05573 |