Practical LLM Evaluation for Production Systems: Measure, monitor, and improve AI system reliability across training and inference

Modern AI systems are expected to do far more than generate fluent text. They should be able to retrieve information, reason through complex problems, understand images and documents, call external tools, execute workflows, and support critical business decisions. Evaluating these systems requires methods that go beyond traditional NLP benchmarks. Taking a product-first approach, this book presents evaluation as a continuous operational capability spanning training, inference, and end-to-end system operation. You'll learn how to connect evaluation metrics directly to deployment gates, rollback criteria, monitoring systems, and production reliability objectives. Using practical examples and real-world workflows, you'll explore evaluation strategies for text LLMs, vision-language models, multimodal conversational systems, mixture-of-experts architectures, reasoning models, agentic systems, retrieval pipelines, Text2SQL and Text2Cypher systems, embedding models, OCR workflows, and guardrail SLMs. You'll also learn how to manage non-determinism, design repeatable test suites, validate tool execution, and measure long-horizon agent behavior in production. By the end of the book, you'll be able to design robust evaluation systems that help teams deploy reliable, safe, and economically viable LLM-powered applications with confidence.

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© 2026 Packt Publishing (E-bog): 9781807423889

Udgivelsesdato

E-bog: 30. juni 2026