The Lifecycle of LLM-as-a-Judge for Large-Scale Recommendation Explanations
LLM-as-a-Judge, which leverages a large language model to evaluate natural language generated by another AI application or model, has become a standard, scalable approach for accelerating and extending costly human evaluation. Yet most work treats a judge as a static artifact, evaluating it once at construction or against a fixed benchmark. We argue instead that an LLM judge operating in a deployed system is better understood as having a lifecycle. It must be built, trained, deployed, and continuously maintained as the surrounding data evolves, and each phase poses distinct technical and operational challenges. We present such a lifecycle for the LLM judges that evaluate recommendation ex...
| Authors | Emma Yanyang Kong, JJ Tan, Ishan Gupta et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 August 2026 |
| Last updated at source | 31 August 2026 |
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