When Your LLM Reaches End-of-Life: A Framework for Confident Model Migration in Production Systems

arXiv cs.AI / 5/1/2026

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Key Points

The article proposes a framework for migrating production LLM systems when the deployed model hits end-of-life or must be replaced.
Its core method uses a Bayesian statistical approach to calibrate automated evaluation metrics against human judgments, improving confidence in model comparisons even with limited manual evaluations.
The framework is demonstrated on a commercial question-answering system handling 5.3M monthly interactions across six global regions, evaluating correctness, refusal behavior, and style adherence.
Results show it can identify suitable replacement models while balancing quality assurance with evaluation efficiency.
The authors argue the approach is broadly applicable to enterprises operating LLM-based products across many models, regions, and use cases as the ecosystem evolves quickly.

Abstract

We present a framework for migrating production Large Language Model (LLM) based systems when the underlying model reaches end-of-life or requires replacement. The key contribution is a Bayesian statistical approach that calibrates automated evaluation metrics against human judgments, enabling confident model comparison even with limited manual evaluation data. We demonstrate this framework on a commercial question-answering system serving 5.3M monthly interactions across six global regions; evaluating correctness, refusal behavior, and stylistic adherence to successfully identify suitable replacement models. The framework is broadly applicable to any enterprise deploying LLM-based products, providing a principled, reproducible methodology for model migration that balances quality assurance with evaluation efficiency. This is a capability increasingly essential as the LLM ecosystem continues to evolve rapidly and organizations manage portfolios of AI-powered services across multiple models, regions, and use cases.