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LLM Observability in Production|Chatvariety Team

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

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Overview

Master LLM Observability: Monitor, Trace, and Evaluate Your AI Systems in Production

As 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 compliance

Whether 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.

This item is Non-Returnable

Details

  • ISBN-13: 9798197071774
  • ISBN-10: 9798197071774
  • Publisher: Independently Published
  • Publish Date: May 2026
  • Dimensions: 9 x 6 x 0.19 inches
  • Shipping Weight: 0.29 pounds
  • Page Count: 90

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