Introduction to Generative AI: Reliable, responsible, and real-world applications. 2 Ed

Introduction to Generative AI, Second Edition....1

brief contents....5

contents....6

foreword....13

preface....15

acknowledgments....17

about this book....19

Who should read this book....20

How this book is organized: A road map....20

liveBook discussion forums....22

Other online resources....22

about the authors....23

about the cover illustration....25

1 Large language models: The foundation of generative AI....26

The evolution of natural language processing....28

The birth of LLMs....32

The explosion of LLMs....34

What are LLMs used for?....36

Language modeling....36

Question answering....38

Coding....39

Content generation....40

Logical reasoning....42

Other natural language tasks....43

Where do LLMs fall short?....44

Training data and bias....44

Limitations in controlling machine outputs....47

Sustainability of LLMs....49

Major players in generative AI....50

OpenAI....51

Google....53

Meta....54

Microsoft ....55

Anthropic....56

Other notable players....57

Conclusion....59

2 Training large language models: Learning at scale....61

How are LLMs trained?....62

Exploring open web data collection....63

Demystifying autoregression and bidirectional token prediction....65

Training multimodal LLMs....66

Transferring knowledge for efficient models....69

Mixture of Experts and sparse models....71

Reasoning models....73

Techniques for post-training LLMs....76

Supervised fine-tuning....77

Reinforcement learning from human feedback....78

Direct preference optimization....79

Reinforcement learning from AI feedback....80

Emergent properties of LLMs....81

Learning with a few examples....82

Is emergence an illusion?....85

Conclusion....86

3 Data privacy and safety: Technical ....88

What’s in the training data?....89

Encoding bias....89

Linguistic diversity....94

Sensitive information....97

Safety-focused improvements for LLM generations....102

Post-processing detection algorithms....103

Content filtering or conditional pretraining....105

Safety post-training....106

Machine unlearning....109

Navigating user privacy and commercial risks....111

Inadvertent data leakage....111

Best practices when interacting with LLMs....114

Data protection and privacy in the age of AI....114

International standards and data protection laws....115

Are generative AI systems GDPR-compliant?....119

Privacy regulations in academia....122

Corporate policies....123

Governing data in an AI-driven world....125

Conclusion....128

4 AI and the creative economy: Innovation and intellectual property....130

The rise of synthetic media....131

Techniques for creating synthetic media....132

The opportunities and risks of synthetic media....137

Detecting synthetic media....139

Transforming creative workflows....144

Marketing and media applications....145

Visual and digital art....148

Filmmaking....149

Music....150

Intellectual property in the LLM era....152

Copyright law and fair use....153

Open source and licenses ....161

Creator’s rights and data licensing ....164

Conclusion....166

5 Misuse and adversarial attacks: Challenges and responsible testing....168

Intentional misuse....169

Cybersecurity and social engineering....170

Illicit and harmful applications....177

Adversarial narratives....185

Political manipulation and electioneering....194

Hallucinations....199

Why do LLMs hallucinate?....199

Misuse of LLMs in the professional world....207

Red teaming LLMs....214

Conclusion....219

6 Machine-augmented work: Productivity, education, and economy....222

Using LLMs in the professional space....223

LLMs assisting doctors with administrative tasks....223

LLMs for legal research, discovery, and documentation....225

LLMs augmenting financial investing and bank customer service....229

LLMs as a programming partner....232

LLMs in daily life....236

Generative AI in education....243

Detecting machine-generated text....249

Generative AI and the labor market....255

Conclusion....260

7 Prompt engineering: Strategies for guiding and evaluating LLMs....262

What is prompt engineering?....263

Prompting techniques and frameworks....269

Overview of common prompting techniques....270

Structuring prompts to guide model behavior....271

Prompting frameworks for structured output....277

Evolving practices in prompt engineering....279

Evaluating AI-generated outputs....283

Identifying evaluation metrics....283

Assembling evaluation datasets....284

Scoring model responses....286

Prompting vs. post-training....291

Conclusion....293

8 AI agents: The rise of autonomous AI systems....295

What is an AI agent?....296

How are AI agents being used?....297

Personal assistants....298

Enterprise workflows....300

Research and discovery....302

Software development....303

Cybersecurity....307

Physical environments....308

Multi-agent systems....309

Toward agentic collaboration....310

How are AI agents trained and enabled?....311

Agent architectures....315

Retrieval-augmented generation....317

Model Context Protocol....320

GUI-native agents....322

Evaluating agents....324

Risks and considerations unique to agents....326

Autonomy and misalignment....327

Memory and state persistence....328

Tool access and real-world consequences....329

Emergent behaviors in multi-agent systems....330

Security and adversarial risks....332

Human factors and decision delegation....333

Evaluation, monitoring, and oversight....334

The road ahead....336

The future of AI agents....336

Conclusion....339

9 Human connections: The social role of chatbots....341

The rise of human–chatbot relationships....342

Why humans are turning to chatbots for relationships....349

The loneliness epidemic....349

Emotional attachment in human–chatbot relationships....352

The benefits and risks of human–chatbot relationships....356

Toward healthier human–chatbot relationships....365

Conclusion....372

10 The future of responsible AI: Risks, practices, and policy....374

Where are LLM developments headed?....375

Language as the universal interface....376

From tools to agentic systems....378

The rise of personalized AI....380

On the horizon....382

Sociotechnical risks of generative AI....384

Bias, toxicity, and representational harms....384

Hallucinations and fabrications....385

Autonomy and emergent agentic risks....387

Misuse across domains....387

Dependency, emotional harm, and relationship risks....388

Labor and economic disruption....389

A holistic view of harm....389

Best practices for responsible AI development and use....390

Curating datasets and standardizing documentation....391

Protecting data privacy....393

Explainability, transparency, and bias....395

Design interventions and architectures....398

Model training strategies for safety....401

Red teaming and evaluation....404

Detecting and tracing synthetic media....405

Platform responsibility and user safeguards....408

Humans in the loop....410

Education and digital literacy....412

Toward responsible generative AI....413

AI regulations in practice....414

The United States....414

The European Union....419

China....424

Corporate self-governance....427

Toward an AI governance framework....430

Conclusion....433

11 Frontiers of AI: Open questions and global trends....435

The quest for artificial general intelligence....436

AI sentience and consciousness....445

The carbon footprint of LLMs....451

The open source movement ....458

Global investment in AI....466

Conclusion....470

references....472

index....503

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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.