- Preface
- Learning Personas and Learning Paths
- Quick Start Checklist (Select Your Persona and Begin)
- Introduction – Looking Under the Hood of Generative AI
-
1. The Geometry of Language - Latent Space, Embeddings, and Drift
- 1.1 Latent Space - The Model’s Map of Meaning
- 1.2 Embeddings - Coordinates in that Space
- 1.3 Semantic Drift - When Meaning Slowly Slides Away
-
2. The Engine - How Transformers Actually Work
- 2.1 Multi-Head Attention – Many “Views” on the Same Text
- 2.2 Context Window - Big Memory, Selective Focus
- 2.3 Information Compression and the Economics of Tokens
-
3. The Controls - Temperature, Sampling, and Determinism
- 3.1 Temperature - The “Risk Dial”
- 3.2 Sampling Strategies - Top-K and Top-P
- 3.3 Entropy - How “Sure” Is the Model?
-
4. Structuring Thought - Prompt Design Beyond “Ask a Question”
- 4.1 System Messages - The Constitution of the Model
- 4.2 Chain-of-Thought and Anchor Thoughts
- 4.3 Controlling Output Format and Length
- 4.4 Complexity and Its Pitfalls
-
5. Systems Architecture - RAG, Tools, and Agents
- 5.1 RAG - Connecting the Model to Real Knowledge
- 5.2 Tools and Hybrid Intelligence
- 5.3 Multi-Agent Systems and Prompt Collisions
-
6. Evaluation, Safety, and Limits
- 6.1 The Alignment Tax - Safety vs. Raw Capability
- 6.2 Evaluating Quality - Beyond Word Overlap
- 6.3 Security Perspective - Training vs. Inference
- Conclusion - From User to Engineer
- Introduction – From Clever Answers to Reliable Systems
-
2.1 Governing Output – Structure, Schemas, and Decisions
- 2.1.1 Why Structure Matters
- 2.1.2 Enforcing Compliance and Stability
- 2.1.3 Turning Free Text into Decisions
-
2.2 Advanced Reasoning Architectures - Keeping Logic on the Rails
- 2.2.1 The Error Cascade Problem
- 2.2.2 Stabilizing Math and Logic
- 2.2.3 Loops, Self-Critique, and Visible Reasoning
-
2.3 Grounding and RAG – Tethering the Model to Reality
- 2.3.1 Why Grounding Beats Personality
- 2.3.2 Citations That Can Be Verified
- 2.3.3 When the Model Disagrees with Your Documents
- 2.3.4 Knowledge Dilution and Multi-Step Retrieval
- 2.3.5 The Multilingual Edge
-
2.4 Agents, Roles, and Multi-Agent Systems
- 2.4.1 Role Prompts and “Bleed-Through”
- 2.4.2 Multi-Agent Workflows and Prompt Collisions
- 2.4.3 Utility Agents and Sandboxes
- 2.4.4 Managing Variance in Agent Chains
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2.5 Domain-Specific Optimization - Law, Medicine, and Translation
- 2.5.1 Long Documents and Progressive Compression
- 2.5.2 Professional Translation with Glossaries
- 2.5.3 Controlling Verbosity and Staying in Domain
-
2.6 Evaluation, Security, and Tuning - Proving It Works
- 2.6.1 Testing Like a Scientist, Not by Vibes
- 2.6.2 Injection, Leakage, and Other Security Risks
- 2.6.3 Reproducibility, State Compression, and Adversarial Testing
- Introduction – From Chatbots to Digital Colleagues
-
3.1 Advanced RAG Architecture – Giving AI a Reliable Memory
- 3.1.1 Retrieval as “Semantic GPS”
- 3.1.2 Chunking - Balancing Precision and Context
- 3.1.3 Hybrid Search – Combining Semantics and Exact Matches
- 3.1.4 Why RAG Still Hallucinates
-
3.2 Building Reliable Agents – Giving AI Hands and Tools
- 3.2.1 How Agents Use Tools
- 3.2.2 Handling Failures and Conflicts
- 3.2.3 Memory, Drift, and State
-
3.3 Orchestrating a Team of Agents
- 3.3.1 Handoffs and Structured Contracts
- 3.3.2 Scheduling Work
- 3.3.3 Verification as the Final Gate
-
3.4 Guardrails and Safety – Building the Shield
- 3.4.1 Defense Against Hallucinations
- 3.4.2 Safety Design Patterns and Constraint Density
- 3.4.3 Prompt Injection and Instruction Integrity
-
3.5 Production Operations - Running the AI Factory
- 3.5.1 Performance, Latency, and Batching
- 3.5.2 Caching - Smart Shortcuts
- 3.5.3 Scaling and Monitoring
- Introduction – The Guardrails of Intelligent Systems
-
4.1 The Human Factor - Trust, Drift, and Reliance
- 4.1.1 The Illusion of Confidence
- 4.1.2 Managing Expectations and Calibrating Trust
- 4.1.3 Task Substitution, Not Full Job Replacement
-
4.2 Governance, Law, and Accountability
- 4.2.1 The Three Pillars of AI Governance
- 4.2.2 Audit Trails and Legal Defensibility
- 4.2.3 The Privacy Paradox of Logging
- 4.2.4 Prompt Stability and Policy Boundaries
-
4.3 Technical Safety and Alignment
- 4.3.1 What Alignment Really Means
- 4.3.2 Bias and Fairness
- 4.3.3 Handling Excessive Refusals
- 4.3.4 Determinism in High-Stakes Domains
-
4.4 High-Stakes Architectures and Failure Modes
- 4.4.1 RAG and the Privacy Leak Problem
- 4.4.2 Legal and Medical Workflows
- 4.4.3 Emergent Risks in Multi-Agent Systems
-
Learning Ecosystem & Resources
- Generative AI Interactive Labs Hub
- Gen AI Exercises Labs Hub
- Gen AI Exam Application
- Gen AI Video Player Application
- Gen AI Flashcards Application
- Gen AI Definitions Applications
- Gen AI Infographics Application
-
Learning Paths
- Path: Everyday AI Navigator
- Path: AI-Enabled Professional
- Path: GenAI Certification Candidate
- Path: Prompt & Template Architect
- Path: RAG & Agentic Systems Product Owner
- Path: Governance & Risk Steward
- Glossary
- Index
- About the Author
The Generative AI Professional Guide
Beyond the Prompt For Everyone
Experience the fundamentals behind the magic of Generative AI then practice them with videos, interactive labs, exercises, flashcards, and an exam app to build outputs you can trust.
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About
About the Book
Artificial intelligence is no longer trapped in labs or buzzwords. Generative AI now shows up in healthcare, workplaces, classrooms, small businesses, and everyday personal life—where real people solve real problems.
Many learners (especially those pursuing certifications or career moves) struggle not because they aren’t capable, but because they’re trying to master advanced material without understanding the fundamentals. This guide exists to fix that—helping you move beyond “AI feels like magic” and into practical understanding you can confidently use.
This is the second guide in the series: the first book The Generative AI Professional Prompt Engineering teaches the How of prompt engineering; this book teaches the What and Why of the mental model underneath the interface how these systems work, where they fail, and how to design more reliable outcomes.
You will build intuition and practical skill around:
- How LLMs represent meaning (latent space, embeddings, and semantic drift)
- How Transformers actually process text (attention, context limits, and token economics)
- How to control randomness vs. determinism (temperature, top-p, and related controls)
- Where structured prompts, RAG, and agentic systems tend to fail and how to spot those failure points
- How to evaluate behavior using truthfulness and safety not just eloquence
This guide is designed to move you from reading to experiencing. Chapter 1 alone connects you to an external learning ecosystem: interactive labs, hands-on exercises, an exam application, a video learning hub, flashcards, a terminology navigator, and an infographics gallery tools built to help you practice, verify, and retain.
Finally, it respects a simple truth: people learn differently. The book supports visual, auditory, read/write, and kinesthetic learning so you can build real understanding in the way that fits you best.
Author
About the Author
George Tome
George Tome is a seasoned Enterprise AI / Agile Coach and Trainer with over 30 years of experience driving innovation and transformation. With a deep expertise in Agile training & methodologies, and global organizational change, George has led groundbreaking initiatives across multiple continents. His work has been recognized both internally and in the Harvard Business Review. George is passionate about developing strategic frameworks and comprehensive training programs that empower teams to harness AI and Agile practices for maximum impact.
Holding certifications in Agile training/coaching (Registered Scrum Trainer) and project management from prestigious institutions including Stanford University, and the University of Chicago, George continues to inspire and lead in the fields of AI and Agile transformation.
Contents
Table of Contents
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