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

Build reliable LLM-powered systems using practical evaluation frameworks, production metrics, and deployment-ready monitoring strategies.

Key Features

• Design evaluation frameworks for production-grade LLM systems
• Measure reliability, safety, latency, and cost across LLM workflows
• Apply unified evaluation methods to text, multimodal, and agentic AI systems
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Move 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 learn

• Design repeatable evaluation pipelines for LLM systems
• Measure inference quality, latency, and operational cost
• Evaluate multimodal, agentic, and reasoning AI systems
• Build regression gates and deployment evaluation workflows
• Detect hallucinations and grounding failures in VLMs
• Assess routing stability in Mixture-of-Experts models
• Evaluate Text2SQL, OCR, and retrieval-based systems
• Translate evaluation signals into production decisions

Who this book is for

ML 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 Contents

1. Foundations of LLM Evaluation: Core Concepts and Primitives
2. Building Reliable Text Only LLMs Through Training Evaluation
3. Controlling Text-Only LLM Behavior at Inference Time
4. Grounding and Reliability in Vision Language Models during Training
5. Evaluating Visual Grounding and Reliability at Inference Time
6. Evaluating Multimodal Conversational LLMs Across Training and Inference
7. Evaluating Routing and Reliability in Mixture of Expert LLM
8. Evaluating Reliability and Control in Computer Using Agent LLM
9. Evaluating Information Extraction and Document Understanding LLMs
10. Evaluating Reasoning LLMs in Depth
11. Evaluating Specialized LLM Systems