Preface
Part I The Foundations of Generative AI

Chapter 1 Ten Breakthroughs That Made Generative AI Possible

Breakthrough 1: The Turing Machine

Breakthrough 2: The Artificial Neuron

Breakthrough 3: The Dartmouth Conference

Breakthrough 4: The Perceptron

The Rise of Symbolic Reasoning (1960s)

The First AI Winter (Early 1970s to Early 1980s)

Breakthrough 5: Neural Networks and Backpropagation

Breakthrough 6: Recurrent Neural Networks

The Second AI Winter (Late 1980s to Mid-1990s)

Breakthrough 7: Invention of the GPU

Breakthrough 8: Reinforcement Learning

Breakthrough 9: Language Modeling

Breakthrough 10: The Transformer

Summary

References
Chapter 2 The Machinery of Learning

Types of Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

The Machine Learning Family Tree

What Is a Model?

How Models Are Trained

Training, Validation, and Test Datasets

Inference Models

How to Measure Model Accuracy

Hyperparameters

Summary
Chapter 3 Foundational Algorithms

Linear Regression: One Stroke to Represent the Data

Describing a Line

Loss Functions and Other Hyperparameters

Classification

Support Vector Machines

Discovering Structures in Data

K-Means, the Clustering King

DBSCAN and Growing Clusters

Summary
Chapter 4 An Introduction to Neural Networks

Neural Networks Key Concepts

ANNs: General Structure and Terminology

Training a Neural Network

Training Models and Overcoming Challenges

The Importance of Clean Data

Labeled Data: The Backbone of Supervised Learning

Avoiding the Pitfalls: Overfitting and Underfitting

Scaling Up Training

Summary
Chapter 5 Neural Network Architectures

Feedforward Neural Networks

Traditional FFNs

Convolutional Neural Networks (CNNs)

Traditional Generative Models

Generative Adversarial Networks (GANs)

Variational Autoencoders (VAEs)

Diffusion Models

Recurrent Models

Recurrent Neural Networks (RNNs)

Long Short-Term Memory Networks (LSTMs)

Summary
Chapter 6 Reinforcement Learning: Teaching Machines to Learn by Trial and Error

An AI That Learns Like Us

Key Concepts of Reinforcement Learning

The Markov Decision Process (MDP)

The Bellman Equation

Model-Based Versus Model-Free Systems

On-Policy Versus Off-Policy Learning: Two Paths to Learning

Monte Carlo Reinforcement Learning

Temporal Difference (TD) Learning

Q-Learning

Deep Reinforcement Learning

Summary

References
Part II The Generative AI Revolution

Chapter 7 Language Modeling: The Birth of LLMs

An Introduction to LLMs

Foundations of Language Modeling

Next-Word Prediction

From Words to Tokens

Word Embedding: Turning Tokens into Numbers

How Word Embeddings Are Learned

Semantic Relationships in the Embedding Space

The Semantics of Language

Summary

Reference
Chapter 8 Attention Is All You Need: The Foundation of Generative AI

A New Architecture Begins to Take Shape

Attention Is All You Need

From Sequential to Parallel Processing

Positional Encoding

The Self-Attention Mechanism

Summary

References
Chapter 9 Attention Isnt All You Need: Understanding the Transformer Architecture

The Encoder Block

The Multi-Head Attention Layer

The Add and Norm Layers and Residual Connections

The Feedforward Network (FFN) Layer

Layers Upon Layers of Encoder Blocks

How Encoders Are Trained

The Decoder Block

The Decoders Output Classifier

How Decoders Are Trained

What Type of Machine Learning Is Involved in Training LLMs?

Case Study: The GPT-3 Transformer

Future Directions

Summary

References
Part III Living with Generative AI

Chapter 10 Making Models Smarter: Prompt and Context Engineering

Prompt and Context Windows

Prompt Engineering Techniques

Shot-Based Approaches

Chain-Based Approaches

Self-Ask Approaches

Prompt Engineering Limitations

Context Engineering

Types of Contexts in LLM Workflows

Tools and Protocols

Context Design Techniques

Summary
Chapter 11 Retrieval-Augmented Generation

The Need for RAG

Common Applications of RAG

RAG Trends and Practices

The RAG Pipeline

Query Formulation

Retrieval Filtering

Working with Knowledge Databases

Loading Documents

Chunking: Splitting Documents

Embedding and Storing Segments

Retrieving Segments

Summary
Chapter 12 Fine-Tuning LLMs

The Need for Fine-Tuning

Comparing Fine-Tuning and RAG

Inference Hyperparameter Tuning for LLMs

Temperature

Top-K Sampling

Top-P (Nucleus) Sampling

Repetition Penalty

Principles of Fine-Tuning with New Data

Fine-Tuning for Model Types and Objectives

Supervised Fine-Tuning (SFT)

Transfer Learning

Parameter-Efficient Fine-Tuning (PEFT) Methods

Retrieval-Augmented Fine-Tuning (RAFT)

Reinforcement Learning from Human Feedback (RLHF)

Benchmarking Model Performance

Summary

References
Chapter 13 Securing LLMs from Attack

What Makes AI Security Different

The Emergence and Importance of AI Security Frameworks

NIST AI Risk Management Framework

The OWASP Top 10

MITRE ATLAS

The ISO/IEC Suite of AI Standards

A Comparison of the AI Security Frameworks

AI Vulnerabilities and Attack Vectors

Direct Prompt Injection Attacks

Prompt Injections with Jailbreaking

Indirect Prompt Injection Attacks

Extraction and Inversion Attacks

AI Supply Chain Threats

Defending Models from Attack

Extending the Guardrail System

Architectural Safeguards

Continuous Monitoring and Detection System

Generative Adversarial Defense Techniques

Summary

References
Chapter 14 AI Ethics and Bias: Building Responsible Systems

Bias and Ethical Risks in GenAI

The Biased Data That Shapes GenAI

The Difficulty of Stopping GenAI Bias

When Generative AI Goes Wrong: Unethical and Harmful Outputs

Hallucination and Misinformation

Synthetic Media, Deepfakes, and Disinformation

Ownership, Consent, and Copyright

Transparency, Explainability, and Trust

Building Responsible Generative AI

Alignment and AI Safety

Practical Responses to Ethical AI Challenges

Summary

References
Chapter 15 The Future of AI: From Generative to General Intelligence

Where We Stand: A Snapshot of Todays Capabilities

Current Limitations and Known Pain Points

The Emergence Question: Are We Seeing Sparks of AGI?

What Makes AGI Different?

What Is AGI?

What Is Intelligence Anyway?

Do Reasoning LLMs Really Reason?

Predicting Versus Understanding

Are We Already on the Path to AGI?

Paths to AGI

The Scaling Hypothesis

The Modular Hypothesis

The Embodied System Hypothesis

Hybrid Models

Is AGI the End of Humanity?

The Alignment Problem Revisited

Black Boxes and Loss of Interpretability

The Singularity and the Skynet Problem

Controlling Existential Risks

Is AGI Helping or Hurting Society?

Will AI Take Your Job?

Education in the Age of Generative AI

Societal Identity and Stability

The Evolving Voice of Generative AI

From Single-Goal Prompting to Multimodal Partnering

Responsive Interfaces

Redefining Creativity

The Future We Choose

Scenario A: The Co-creative Society

Scenario B: The Automated Present

Scenario C: The Disrupted Path

Summary

References
Appendix A The History of AI
Appendix B A Summary of Neural Network Model Architectures
Glossary

9780135429419 TOC 12/19/2025