Search results for "Mastering Prompt Engineering for Generative AI"
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ARTIFICIAL INTELLIGENCE FOR ALL: A HANDBOOK ON PROMPTING AND AGENTS
Artificial Intelligence for All: A Handbook on Prompting and Agents is a comprehensive practical guide designed for the age of Large Language Models (LLMs) and AI Agents. Centered on the core concepts of prompting, AI cognition, and intelligent agent collaboration, the book provides a complete framework that bridges foundational AI understanding with real-world execution.Starting from the fundamentals of AI communication and prompt engineering, the book systematically explores cognitive differences between humans and AI systems, advanced prompt structures, AI workflow design, and industry-specific AI applications. It further expands into AI Agent orchestration, multi-modal systems, local deployment, privacy protection, and AI governance.Unlike conventional introductory AI books that focus only on concepts or tools, this handbook emphasizes practical implementation through reusable frameworks, real-world case studies, structured prompt methodologies, and deployment strategies. Readers will learn how to transform AI from a passive chatbot into an active collaborative assistant capable of planning, reasoning, automation, and decision support.With applications across education, healthcare, finance, marketing, and enterprise productivity, this book is suitable for general readers, professionals, universities, corporate training programs, and organizations seeking AI transformation.
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ARTIFICIAL INTELLIGENCE FOR ALL: A HANDBOOK ON PROMPTING AND AGENTS
Artificial Intelligence for All: A Handbook on Prompting and Agents is a comprehensive practical guide designed for the age of Large Language Models (LLMs) and AI Agents. Centered on the core concepts of prompting, AI cognition, and intelligent agent collaboration, the book provides a complete framework that bridges foundational AI understanding with real-world execution.Starting from the fundamentals of AI communication and prompt engineering, the book systematically explores cognitive differences between humans and AI systems, advanced prompt structures, AI workflow design, and industry-specific AI applications. It further expands into AI Agent orchestration, multi-modal systems, local deployment, privacy protection, and AI governance.Unlike conventional introductory AI books that focus only on concepts or tools, this handbook emphasizes practical implementation through reusable frameworks, real-world case studies, structured prompt methodologies, and deployment strategies. Readers will learn how to transform AI from a passive chatbot into an active collaborative assistant capable of planning, reasoning, automation, and decision support.With applications across education, healthcare, finance, marketing, and enterprise productivity, this book is suitable for general readers, professionals, universities, corporate training programs, and organizations seeking AI transformation.
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Prompt Engineering for LLMs
Large language models (LLMs) promise unprecedented benefits. Well versed in common topics of human discourse, LLMs can make useful contributions to a large variety of tasks, especially now that the barrier for interacting with them has been greatly reduced. Potentially, any developer can harness the power of LLMs to tackle large classes of problems previously beyond the reach of automation.This book provides a solid foundation of LLM principles and explains how to apply them in practice. When first integrating LLMs into workflows, most developers struggle to coax useful insights from them. That's because communicating with AI is different from communicating with humans. This guide shows you how to present your problem in the model-friendly way called prompt engineering.With this book, you'll:Examine the user-program-AI-user model interaction loopUnderstand the influence of LLM architecture and learn how to best interact with itDesign a complete prompt crafting strategy for an application that fits into the application contextGather and triage context elements to make an efficient promptFormulate those elements so that the model processes them in the way that's desiredMaster specific prompt crafting techniques including few-shot learning, and chain-of-thought prompting
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Generative AI for Connected and Autonomous Vehicles
Harness generative AI for connected and autonomous vehicle systems As generative AI reshapes intelligent transportation, engineers and researchers need rigorous frameworks for deploying these technologies in safety-critical systems. This book offers a comprehensive treatment of the models, protocols, and adaptation techniques required to bring large language models, vision-language models, diffusion models, and agentic systems into modern CAV architectures. The book progresses from generative foundations through multimodal representation, language-based interaction, agentic coordination, communication protocols, model adaptation, and validation and safety. Mathematical, probabilistic, and reinforcement-learning prerequisites are consolidated into dedicated appendices, allowing the main chapters to focus on system design and CAV-specific applications. Written by an award-winning researcher with more than 100 patents across connected vehicles and AI, readers will also find: Practical treatment of prompt engineering, retrieval-augmented generation, function calling, and tool use for grounding GenAI in vehicular contextChapters on agent architectures, multi-agent systems, orchestration frameworks, and MCP and A2A protocols for collaborative CAV intelligenceAdaptation and deployment techniques including supervised fine-tuning, reinforcement learning fine-tuning, and knowledge distillation for resource-constrained edge platformsDedicated chapters on simulation platforms, evaluation methodologies, and ethical and safety considerations for deploying generative AI in autonomous vehicles Automotive engineers, AI researchers, and graduate students at the intersection of machine learning and intelligent transportation will find this book indispensable. Readers from OEMs, mobility startups, or academic labs gain the foundations and practices needed to design, adapt, and validate generative AI in connected mobility.
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