When Your LLM Reaches End-of-Life: A Framework for Confident Model Migration in Production Systems
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 produc...
| Authors | Emma Casey, David Roberts, David Sim et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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