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AI systems fail in unique ways: silent degradation, hallucination spikes, and embedding drift. This course teaches observability specifically for AI — LLM tracing, output monitoring, and anomaly detection.

✅ What’s Inside:

  1. AI Observability vs Traditional Monitoring
  2. LLM Request Tracing
  3. Prompt and Response Logging
  4. Latency and Cost Dashboards
  5. Hallucination Rate Monitoring
  6. Output Quality Drift Detection
  7. Embedding Drift Detection
  8. Alerting for AI Anomalies
  9. OpenTelemetry for AI
  10. LangSmith and Similar Tools
  11. Incident Response for AI
  12. Project: Full Observability Stack for an AI App
  13.