Traditional monitoring tells you if the server is up. LLM observability tells you if the answers are still good. Here is how to watch AI quality in production.
Binate AI
November 20, 2025
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LLM observability is the practice of capturing, measuring, and alerting on the quality and behavior of an AI system in production — not just uptime and latency, but answer quality, hallucination rate, cost per request, and user feedback. It answers "is the AI still good?", which standard APM cannot.
Log every step: the prompt, retrieved context, tool calls, model output, latency, tokens, and cost. Without traces you cannot debug a bad answer or explain a decision later.
Action Checklist
0/5Quality degrades silently as data and usage shift. Run a sample of production traffic through your eval set continuously, and alert when scores, refusal rates, or cost drift beyond bounds.
LLM observability adds something APM lacks.
Quick Quiz
We instrument LLM systems so quality stays measured and protected.
Assess your AI readinessLog full traces, run continuous evals on live traffic, and alert on quality drift. Uptime is table stakes; answer quality is the real SLA.
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