Tag: observability

Traditional monitoring catches when systems are down but misses when AI is confidently wrong. You need different metrics for systems that fail quietly - tracking output quality, user satisfaction, and model drift alongside uptime. Learn how to build ai observability monitoring that catches problems before users complain.

Traditional monitoring tells you if your LLM is running. It does not tell you if it is delivering garbage to users. Quality degradation is silent, and traditional metrics miss it completely. Here is how to monitor what actually matters for LLM applications in production.