Practical Benchmarking and Hands-On Evaluation of AI Popular Systems: A Comparative Experimental Guide to Modern Platforms

Practical Benchmarking and Hands-On Evaluation of AI Popular Systems is a companion volume to AI Popular Systems: Architecture, Capabilities, and Comparative Analysis. While the first book examines the design, features, integration, and broader characteristics of major AI platforms, this volume evaluates their practical performance through a shared experimental framework. Using CNN development on the MNIST dataset as a common task, the book compares systems such as ChatGPT, Claude, Gemini, Copilot, Meta Llama, Gemma, Mistral, Hugging Chat, and Zapier. It examines prompt effectiveness, code generation, debugging support, model design, training, visualization, optimization, and final accuracy. The volume combines reproducible experiments, code examples, figures, and comparative observations to show how modern AI assistants perform in realistic machine learning workflows. It is intended for students, researchers, educators, AI engineers, and practitioners interested in applied evaluation of generative AI systems.