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Monitoring & Observability Guide
Master the observability of AI systems in production with OpenTelemetry, the industry standard for 2026. Learn to instrument LLM applications with traces for prompts, embeddings, and tool calls, build dashboards for latency and cost, design alerting strategies, implement AI-specific monitoring (prompt quality, token usage, model drift), and debug production issues like hallucinations and cost spikes. Integrates with LangSmith and monitoring backends.
- 64
- lessons
- 8
- modules
- English · Spanish
- available in
- Yes
- certificate
- Included in the Club
- access
Outcomes
What you'll be able to do
- Understand observability for AI systems vs generic monitoring (logs, metrics, traces)
- Instrument key metrics: latency (TTFT, TTI), cost (tokens, USD/request), errors, output quality
- Set up OpenTelemetry and trace prompts, embeddings, and tool calls (industry standard 2026)
- Build dashboards for AI operations (latency percentiles, cost trends, error breakdown)
- Design alerting strategies for AI (cost spikes, latency SLO breaches, error rate)
- Implement AI-specific monitoring: prompt quality, token usage, model drift, hallucination detection
- Integrate LangSmith with OpenTelemetry for LLM flow debugging
- Debug production AI issues: hallucinations, cost spikes, latency with runbooks and trace analysis
Before you start
What you need to bring
It's for you if...
- AI Engineers with deployed systems who need to observe, monitor, and debug in production
- Backend developers operating AI APIs who need dashboards and alerts for SLA
- Tech leads preparing AI systems to scale with visibility into cost, latency, and quality
- Teams adopting OpenTelemetry as the instrumentation standard for AI systems
- Developers who want to differentiate with production-grade AI observability skills
Requirements and materials
- Production Best Practices Guide (#13) — testing, guardrails, structured logging
- Docker Essentials (#15) — containerization
- Deployment & Cloud Infrastructure (#17) — deployed AI systems (local, cloud, or serverless)
- Python intermediate, FastAPI or equivalent
- At least one AI system running in production or near-production staging
Content
The syllabus, module by module
Open any of them to see its lessons.
- 1. Introduction: Observability for AI Systems
- 2. Observability vs Monitoring
- 3. Why AI Needs Different Observability
- 4. The Three Pillars in AI Context
- 5. Gaps in AI Systems Without Observability
- 6. The Observability Mindset
- 7. Prioritizing Instrumentation
- 8. Project: Observability Assessment
- 1. Introduction: Key Metrics for AI
- 2. Latency: TTFT, TTI, End-to-End
- 3. Cost: Tokens, USD per Request
- 4. Errors: Rate, Types, Retries
- 5. Output Quality: Relevance and Hallucination Score
- 6. SLOs for AI Systems
- 7. Derived Metrics and Instrumentation
- 8. Project: Metrics Instrumentation
- 1. Introduction: OpenTelemetry Setup and Instrumentation
- 2. OpenTelemetry as the Standard
- 3. Python SDK Setup
- 4. Tracing Prompts and Completions
- 5. Spans for Embeddings and Tool Calls
- 6. Distributed Context and Propagation
- 7. Semantic Conventions for gen_ai
- 8. Project: OTel Instrumented AI App
- 1. Introduction: Dashboards and Visualization
- 2. Designing Dashboards for AI Operations
- 3. Prometheus + Grafana Setup
- 4. Latency Panels
- 5. Cost and Token Panels
- 6. Error and Quality Panels
- 7. Drill-down and Correlation with Traces
- 8. Project: AI Operations Dashboard
- 1. Introduction: Alerting Strategies
- 2. Designing Alerts for AI
- 3. Cost Alerts
- 4. Latency Alerts and SLO Breaches
- 5. Error Rate and Quality Alerts
- 6. Multi-Signal Alerts and Correlation
- 7. Alert Fatigue Prevention
- 8. Project: Alerting Strategy
- 1. Introduction: AI-Specific Monitoring
- 2. Prompt Quality Tracking
- 3. Detailed Token Usage Monitoring
- 4. Model Drift Detection
- 5. Hallucination Detection
- 6. LangSmith: Setup and Integration
- 7. Custom Metrics and AI-Specific Dashboards
- 8. Project: AI-Specific Monitoring Layer
- 1. Introduction: Debugging Production AI Issues
- 2. Debugging Hallucinations
- 3. Investigating Cost Spikes
- 4. Diagnosing Latency Degradation
- 5. Trace-Driven Debugging Methodology
- 6. Prompt Inspection in Production
- 7. Post-Mortem for AI Incidents
- 8. Project: Production Debug Runbook
- 1. Introduction: Capstone Project — Observability Stack
- 2. Integration Architecture
- 3. End-to-End Pipeline
- 4. Validation with Simulated Incidents
- 5. Sampling and Observability Cost
- 6. Documentation and Onboarding
- 7. Stack Maintenance
- 8. Project: Full Observability Stack
Where it fits
This guide is part of something bigger
It's studied inside these programs, with support and dates.
Common questions
What people usually ask
As long as your Club subscription is active. If you cancel and come back later, you get the access and your progress back.
No. Modules run from easier to harder, but you can jump to the one you need. Progress is saved per lesson.
Whatever is needed is listed under “What you need to bring”, above. If nothing is listed there, you can start from zero.
In the Club's WhatsApp group, and every two weeks there's a live with an instructor where questions get worked through.
Yes. It's issued automatically once you finish every lesson, with a verifiable code you can share on LinkedIn.
No. This guide is self-paced with no dates. The bootcamp is live, by cohort, with work someone reviews.
Start whenever you like
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