Current Methods for Testing and Evaluation of AI and LLM Systems

Artificial Intelligence (AI) and Large Language Models (LLMs) are transforming every industry they touch — but how do we know these systems are safe, reliable, and fair? In Current Methods for Testing and Evaluation of AI and LLM Systems, Gene Arguelles delivers a groundbreaking guide for researchers, developers, engineers, and policymakers working at the forefront of AI innovation. This book explores today’s most advanced evaluation frameworks, from foundational metrics like accuracy, precision, and recall, to cutting-edge adversarial testing, fairness audits, prompt sensitivity analyses, and agent-based evaluations. Special attention is given to dynamic, real-time evaluation methods used in regulated industries such as healthcare and defense, where continuous oversight is critical. With detailed case studies — including AI integration in Class III medical devices and GPT-powered neurocognitive assessment tools for military healthcare — this book goes beyond technical formulas to address the ethical, regulatory, and operational challenges of AI deployment. Whether you are designing robust evaluation pipelines, conducting bias audits, or preparing your models for real-world rollout, this authoritative resource offers the tools and insights needed to navigate the complex landscape of modern AI evaluation. Prepare your systems for tomorrow by mastering the evaluation methods that matter today.