LLM Observability in Production: Monitoring, Tracing, and Evaluating AI Systems

Master LLM Observability: Monitor, Trace, and Evaluate Your AI Systems in ProductionAs large language models move from research prototypes to business-critical production systems, the ability to observe, understand, and continuously improve their behavior has become a core engineering competency. This comprehensive guide delivers everything you need to build world-class observability for LLM systems-from foundational instrumentation to advanced evaluation automation.- Instrument LLM pipelines with OpenTelemetry and semantic conventions for vendor-neutral tracing- Deploy Langfuse for full-stack observability including prompt version management and A/B testing- Implement RAGAS and DeepEval for automated faithfulness, relevance, and hallucination evaluation- Monitor multi-agent and agentic workflows with trajectory quality assessment- Use Arize Phoenix for embedding drift detection and local debugging- Build evaluation datasets, human feedback loops, and fine-tuning data pipelines- Design production infrastructure for scalability, security, and complianceWhether you are an ML engineer building your first production LLM system or a senior architect designing observability infrastructure for a large AI platform, this book provides the practical frameworks, code patterns, and organizational practices that separate high-performing AI teams from those flying blind. Written for working engineers in the AI and software engineering field.