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If your customers depend on your AI to be right, your retrieval augmented generation pipeline is either earning trust or eroding it on every query. The difference often comes down to what you measure and how quickly you act on it. This guide shows you how to build a rigorous,

AI evaluation has shifted from static correctness checks to dynamic, context-aware judgment. As applications evolve beyond single-turn prompts into complex agents, tool use, and multi-step workflows, teams need evaluation that mirrors how users actually experience AI. Enter “LLM as a Judge” — using a model to evaluate other models or agents.

TL;DR Enterprise AI evaluation must cover three layers end to end: experiment, evaluate, and observe. Choose a platform that unifies offline evals, agent simulations, and online evals in production, and integrates with your observability stack. Priorities for 2025 include OpenTelemetry compatibility, human-in-the-loop pipelines, dataset curation from production logs, and