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LLM Observability in Production: Monitoring, Tracing, and Evaluating AI Systems
Paperback
Series: Production AI Engineering
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Publisher: Independently Published
Pages: 90
Weight: 0.29
Height: 0.19 Width: 6.00 Depth: 9.00
Language: English
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.
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