Uptime and error rate are not enough. This post covers the metrics that actually reveal whether your LLM is working correctly in production — time-to-first-token, cost per request, hallucination rate indicators, output drift, and how to build dashboards that catch silent failures before users do.
Category: LLMOps
Distributed Tracing for LLM Applications with OpenTelemetry
You cannot fix what you cannot see. This post walks through instrumenting a full LLM pipeline with OpenTelemetry in Node.js, Python, and C# — capturing every span from user request through retrieval, model call, tool execution, and response.
Why LLMOps Is Not MLOps: The New Operational Reality for AI Teams
Most teams try to apply their existing MLOps practices to LLMs and hit a wall fast. This post breaks down exactly why LLMOps is a different discipline, where the gaps are, and what the new operational stack looks like in production.
