LLM evaluation: how to measure hallucination rate in production
Hallucination — when a model confidently asserts things that are factually wrong or unsupported by source material — is the primary reliability concern for LLM applications in production. This article covers how to define, measure, and track hallucination rates using automated evaluation pipelines and LLM-as-judge patterns.
llmevaluationhallucinationtestingobservability
16 July 2026
LLM observability: tracing, logging, and alerting for AI applications
LLM applications fail in ways that traditional APM tools do not capture. This article covers the full observability stack for AI systems — distributed tracing across multi-step pipelines, structured logging for prompt and response data, and alerting strategies that distinguish model quality regressions from infrastructure incidents
llmobservabilitytracingloggingmonitoring
16 July 2026