Beyond LLM-as-a-judge: Establishing LLM evaluations as a foundation for trustworthy agentic AI systems

This article explores how LLM evaluations provide a systematic framework for measuring the quality, accuracy, and safety of probabilistic AI models as they move from experimentation to production. It distinguishes the broader evaluation process from specific scoring methods like "LLM-as-a-Judge" and emphasizes that a robust strategy requires combining various code-based, model-based, and human-based approaches to address challenges like hallucinations.