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Module 11: AI Observability Engineering Slides

Slide walkthrough for Module 11 of Production-Grade RAG Systems Engineering: LLM tracing, token monitoring, cost tracking, and production AI telemetry....

This slide page is the visual review companion for the full course module. Use it to recap the architecture, examples, exercises, production warnings, and takeaways after reading the lesson.

Slide Outline

  1. AI Observability Engineering - LLM tracing, token monitoring, cost tracking, and production AI telemetry
  2. Learning Objectives - 4 outcomes for this module
  3. Why This Module Matters - AI systems are expensive to run and hard to debug without observability. A single misconfigured query expansion can 10x
  4. LLM Tracing - Lesson section from the full module
  5. Token Monitoring - Lesson section from the full module
  6. Cost Monitoring - Lesson section from the full module
  7. Hands-On Labs - 2 hands-on labs
  8. Key Takeaways - 5 points to remember

Learning Objectives

  • Instrument RAG systems with OpenTelemetry
  • Trace requests through the full RAG pipeline
  • Monitor token usage and LLM costs
  • Build AI-specific observability dashboards

Why This Module Matters

AI systems are expensive to run and hard to debug without observability. A single misconfigured query expansion can 10x your token costs. A model update can silently degrade quality. Observability catches these before users do.

Key Takeaways

  • Trace every RAG step: embed, retrieve, generate — know where time is spent
  • Monitor token usage per request — LLM costs are your largest expense
  • Track cost per tenant for multi-tenant systems
  • Quality metrics (retrieval precision, groundedness) should be continuous
  • Alert on cost spikes, latency degradation, and quality drops

Hands-On Labs

  1. Add Tracing to Your RAG Pipeline

    Instrument with OpenTelemetry for full request tracing.

    30 min - Intermediate

    • Add OpenTelemetry SDK to your RAG service
    • Create spans for embed, retrieve, generate steps
    • Export to Jaeger for trace visualization
    • Identify latency bottlenecks

    View lab files on GitHub

  2. Build Cost and Quality Dashboards

    Monitor token usage, costs, and quality metrics.

    30 min - Intermediate

    • Export token metrics to Prometheus
    • Build Grafana dashboards for cost per request and per tenant
    • Add quality score tracking over time
    • Set up alerts for cost spikes and quality drops

    View lab files on GitHub

Read the full module | Back to course curriculum