LLM Evals vs Observability: Why You Need Both

LLM evaluation and LLM observability are complementary but distinct practices: evaluations assess whether an application’s outputs meet defined quality criteria, while observability captures how the application behaved in production through traces, latency, token usage, costs, errors, retrievals, and tool calls. Offline product evaluations use curated test sets to detect regressions before release, whereas online evaluations score sampled live traffic to identify production drift and unexpected failures; LLM-as-a-judge is commonly used for open-ended outputs but should be calibrated against human labels and interpreted as a trend signal due to known biases. Neither discipline is sufficient alone, since successful tests can miss changing real-world conditions and healthy operational dashboards cannot identify fluent but incorrect answers. The recommended approach is to instrument applications with OpenTelemetry GenAI conventions, store production traces in an observability system, asynchronously evaluate a controlled sample of traces, write quality scores back as telemetry linked by trace ID, and alert on quality declines alongside latency or cost anomalies. Failed traces and negative user feedback can then be incorporated into offline golden datasets, creating a feedback loop in which production failures strengthen future regression testing. OpenObserve is presented as an open-source observability platform that supports GenAI trace analysis and can store evaluation scores, while its enterprise offering provides managed online evaluation workflows; it can be paired with separate evaluation libraries for offline testing and dataset management.

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