Practical LLM Evaluation for Production Systems: Measure, monitor, and improve reliable LLM systems across training and inference

Build reliable LLM-powered systems using practical evaluation frameworks, production metrics, and deployment-ready monitoring strategies.Key FeaturesDesign evaluation frameworks for production-grade LLM systemsMeasure reliability, safety, latency, and cost across LLM workflowsApply unified evaluation methods to text, multimodal, and agentic AI systemsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMove beyond benchmarks and learn how to evaluate whether LLM-powered systems actually work in production. This book gives you practical frameworks, metrics, and operational strategies to measure reliability, safety, quality, latency, and cost across modern AI systems. Guided by experienced AI leaders and researchers, you’ll build evaluation pipelines that support real business decisions instead of isolated leaderboard scores.The book takes a product-first approach to evaluation, treating it as a continuous operational capability rather than a one-time testing exercise. You’ll explore how evaluation changes across training, inference, and end-to-end system operation while learning how to connect metrics directly to deployment gates, rollback criteria, monitoring systems, and production reliability goals.Using practical examples and real-world workflows, the book covers evaluation strategies for text LLMs, vision-language models, multimodal conversational systems, Mixture-of-Experts architectures, agentic systems, reasoning models, Text2SQL and Text2Cypher systems, retrieval pipelines, embedding models, OCR workflows, and guardrail SLMs.By the end of this book, you’ll be able to design and operate reliable, safe, and cost-effective LLM-powered applications with confidence.What you will learnDesign repeatable evaluation pipelines for LLM systemsMeasure inference quality, latency, and operational costEvaluate multimodal, agentic, and reasoning AI systemsBuild regression gates and deployment evaluation workflowsDetect hallucinations and grounding failures in VLMsAssess routing stability in Mixture-of-Experts modelsEvaluate Text2SQL, OCR, and retrieval-based systemsTranslate evaluation signals into production decisionsWho this book is forML engineers, GenAI engineers, AI architects, data scientists, platform engineers, and engineering managers responsible for deploying LLM-powered systems in production will benefit from this book. Applied AI researchers and technical decision-makers looking to measure reliability, safety, and operational readiness across modern AI systems will also find it valuable. Readers should have a working understanding of machine learning, Python, and modern LLM concepts.Table of ContentsFoundations of LLM Evaluation: Core Concepts and PrimitivesBuilding Reliable Text Only LLMs Through Training EvaluationControlling Text-Only LLM Behavior at Inference TimeGrounding and Reliability in Vision Language Models during TrainingEvaluating Visual Grounding and Reliability at Inference TimeEvaluating Multimodal Conversational LLMs Across Training and InferenceEvaluating Routing and Reliability in Mixture of Expert LLMEvaluating Reliability and Control in Computer Using Agent LLMEvaluating Information Extraction and Document Understanding LLMsEvaluating Reasoning LLMs in DepthEvaluating Specialized LLM Systems Read more