Evaluating LLM Output Quality at Scale in Production
How we build automated evaluation pipelines using LLM-as-a-judge, semantic similarity metrics, and human-in-the-loop audit logs.
Evaluating generative models is hard because standard tests like accuracy don't apply. Instead, we use semantic evaluation pipelines that measure context precision and faithfulness dynamically.
We review tracing requests with OpenTelemetry, calculating answer relevance scores using judge models, and building lightweight feedback widgets to capture real user interactions in production.