Production LLM Observability
Detect AI-feature regressions in 14 minutes, not 18 hours.
Standard SRE observability tells you when the service is down. It does not catch refusal-rate drift, response-length anomalies, retrieval-precision decay, or tool-call distribution shifts. This course covers the four LLM-specific signals, the trace schema that makes incidents debuggable, hourly probe sets, the on-call playbook for AI features, choosing an observability stack, privacy in traces, and a capstone that wires it all into one real feature.
7h
Duration
8
Lessons
760
Learners
Course map
Lessons unlock as you complete the previous one. Your progress is saved on this device.
Lesson 1
Why observability is different for LLMs
9m35 XPLesson 2
The trace — what to capture per request
11m40 XPLesson 3
Metrics that correlate with user complaints
10m40 XPLesson 4
Drift detection on retrieval and outputs
9m40 XPLesson 5
The on-call playbook for AI features
10m40 XPLesson 6
Choosing an observability stack
9m35 XPLesson 7
Privacy in observability — what to redact, what to keep
8m35 XPLesson 8
Capstone — wiring observability into a real feature
11m50 XP
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