AI Evaluation Engineering
A Practical Guide to Evaluating LLMs, RAG, Agentic AI, and Production GenAI Systems
How do you know whether your AI system is accurate, reliable, safe, and ready for production?
As enterprises rapidly adopt Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI, evaluating AI systems has become as important as building them.
AI Evaluation Engineering is a comprehensive, practical guide to systematically measuring, testing, monitoring, and improving modern Generative AI applications. Designed for AI engineers, data scientists, machine learning engineers, AI product managers, and enterprise architects, this book bridges the gap between AI experimentation and production-ready systems.
What You'll Learn
AI Evaluation Fundamentals: Design evaluation strategies, metrics, datasets, golden sets, and quality benchmarks.
LLM-as-a-Judge: Build automated evaluators, develop scoring rubrics, calibrate judges, and measure evaluation reliability.
RAG Evaluation: Measure retrieval precision, recall, faithfulness, groundedness, relevance, and citation accuracy.
Agentic AI Evaluation: Assess tool calling, planning, reasoning, memory, multi-agent coordination, and task completion.
AI Safety and Guardrails: Evaluate hallucinations, prompt injection, adversarial attacks, privacy risks, and responsible AI controls.
Production AI Evaluation: Implement regression testing, CI/CD evaluation gates, observability, monitoring, and continuous improvement.
Performance Optimization: Balance AI quality, latency, token usage, model performance, and operational costs.
Enterprise AI Governance: Establish evaluation frameworks, human oversight, auditability, and production release criteria.
From Theory to Real-World Implementation
Featuring 35 chapters, practical engineering frameworks, Python examples, evaluation checklists, and enterprise case studies, this book demonstrates how to evaluate AI applications across banking, public-sector document intelligence, customer service, enterprise knowledge systems, and multi-agent workflows.
Whether you're developing your first RAG application or managing enterprise-scale Agentic AI platforms, this book provides the concepts and practical methodologies needed to build AI systems that are measurable, trustworthy, scalable, and continuously improving.
Move beyond AI demos. Build AI systems you can measure, trust, and confidently deploy.
