Synopsis
Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.
AI tools like ChatGPT and Gemini, automated coding tools like Cursor and Copilot, and countless LLM-powered agents have become a part of daily life. They’ve also spawned a storm of misinformation, hype, and doomsaying that makes it tough to understand exactly what Generative AI actually is and what it can really do. This book delivers a clear, well-written survey of generative AI fundamentals along with the techniques and strategies you need to use AI safely and effectively.
It guides you from your first eye-opening interaction with tools like ChatGPT to how AI tools can transform your personal and professional life safely and responsibly. AI moves fast—and so this second edition has been completely revised to reflect the latest developments in the field.
In this easy-to-read introduction, you’ll learn:
• How large language models (LLMs) work
• How to apply AI across personal and professional work
• The social, legal, and policy landscape around generative AI
• Emerging trends like reasoning models and vibe coding
About the technology
Generative AI tools like ChatGPT, Gemini, and Claude can draft emails, generate marketing copy, and prototype product designs. They can also produce poetry, realistic images or videos, and even generate computer code. But how do they do all that? This accessible book reveals how generative AI works in plain, jargon-free language, so you can use it safely and effectively.
About the book
Introduction to Generative AI, Second Edition is a completely revised and updated guide to the capabilities, risks, and limitations of generative AI. You’ll understand the latest innovations in AI, AI agents, multimodal training, reasoning models, retrieval-augmented generation (RAG), and more. Along the way, you’ll explore how AI is impacting the world, with an expert-level look at AI in industry, education, and society.
What's inside
• How AI and foundation models work
• Applications across daily life and work
• Balancing innovation with responsibility
About the reader
No technical experience required.
About the author
Numa Dhamani is a natural language processing expert working at the intersection of technology and society. Maggie Engler is a researcher and engineer working on safety for generative AI systems.
Table of Contents
1 Large language models: The foundation of generative AI
2 Training large language models: Learning at scale
3 Data privacy and safety: Technical and legal controls
4 AI and the creative economy: Innovation and intellectual property
5 Misuse and adversarial attacks: Challenges and responsible testing
6 Machine-augmented work: Productivity, education, and economy
7 Prompt engineering: Strategies for guiding and evaluating LLMs
8 AI agents: The rise of autonomous AI systems
9 Human connections: The social role of chatbots
10 The future of responsible AI: Risks, practices, and policy
11 Frontiers of AI: Open questions and global trends
About the Author
Numa Dhamani is a natural language processing expert with domain expertise in information warfare, security, and privacy. She has developed machine learning systems for Fortune 500 companies and social media platforms, as well as for startups and nonprofits. Numa has advised companies and organizations, served as the Principal Investigator on the United States Department of Defense’s research programs, and contributed to multiple international peer-reviewed journals.
Maggie Engler is an engineer and researcher currently working on safety for large language models. She focuses on applying data science and machine learning to abuses in the online ecosystem, and is a domain expert in cybersecurity and trust and safety. Maggie is also an adjunct instructor at the University of Texas at Austin School of Information.
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