Generative AI in a Nutshell
Generative AI in a Nutshell
How to Survive and Thrive in the Age of AI
About the Book
Welcome to the strange new world of Generative AI! This book is a fast-paced, practical, and mostly human-written guide to what the heck is going on, and what you can do about it. It is like an extended version of Henrik's viral video with the same name.
This book covers questions like: What is generative AI? How does it work? How do I use it? What are some of the risks & limitations? It also covers topics like how to lead an AI transformation, autonomous agents, the role of us humans, prompt engineering tips, AI-powered product development, different types of models, and some tips about mindset and how not to freak out.
Everything is explained in plain English with Henrik's signature hand-drawn illustrations and concrete real-life examples. Minimum use of jargon and buzzwords.
Don't just survive the Age of AI — learn how to thrive in it!
About the Author
Reader Testimonials
Marty Cagan
Founder, Silicon Valley Product Group
Henrik truly has a gift. He's able to quickly learn, and then explain, nuanced and complex topics in ways that immediately resonate with people. And there is perhaps no more important subject to apply this talent than Generative AI. I hope you’ll give this book a read, and share it with your colleagues.
Table of Contents
- Foreword by Egbert
- Computers Have Gotten Smarter
- Einstein in Your Basement
- Terminology
- How it Works
- Reasoning models
- Training
- Models, Models Everywhere
- AI clients vs AI models
- You get what you pay for
- Limitations
- Cut-off Date
- Lack of memory & limited context
- Hallucinations
- Calculation
- The Big Picture
- Model Types
- Text to Text
- Text to Image
- Image to Image
- Image to Text
- Audio to Text
- Text to Audio
- Audio to audio
- Text to Video
- Multimodal Models
- Example: Is my bacon done?
- Example: Where can I hide stuff?
- Example: The drunk funny tutor
- Example: How do I configure the &%#€ firewall?
- Example: Taking AI for a walk
- Emergent Capabilities
- The Age of AI
- What about energy consumption?
- Utopia or Dystopia?
- Mindset
- More time for humans to do human stuff
- The Role of Humans
- You are more than your job
- AI as your colleague
- Task automation, not job automation
- What if your job is at risk?
- Recalibrate
- Ask AI how it can help you
- Building AI-Powered Products
- Building your own AI products
- Build or buy?
- Example 1: Talking to GPT
- Example 2: Building a CV analyzer
- Reflection
- Prompt Engineering
- Example - from bad to good prompt
- The biggest limitation is you
- How to learn Prompt Engineering
- Will prompt engineering still matter when models improve?
- Do I need to be nice to my AI?
- Autonomous Agents with Tools
- Agent = LLM + Tools + Autonomy
- Example 1: a bug fixing agent
- Example 2: Incident management agent
- When to use agents
- Embodied agents
- Agent safety
- Future of AI agents
- Part 1 Wrap up
- Experiment!
- My Journey into AI
- Make Generative AI useful
- Studying Artificial Neural Networks
- Coding Minecraft
- Chat GPT
- Aha 1: It can code like a pro!
- Aha 2: It can write like a pro!
- Maybe we’re not as creative and intelligent as we think
- Leading an AI transformation
- What is an AI transformation, and why is it important?
- Top down or Bottom up?
- Assign an AI Leader
- Access, Experiment, Leverage
- Step 1: Access
- Step 2: Experimentation
- Step 3: Leverage
- Watch out for wasteful IT projects
- Be a role model
- Avoid using AI to fire people
- Reflection
- Interlude: Egbert’s Life Story
- Prompt Engineering Techniques
- Mind the context window & prompt length
- Iteration techniques
- Technique: Self-reflection prompt
- Elements of a good prompt
- Start high level, then get into details
- How smart of a model do you need?
- Prompt engineering is an evolving field
- Prompt Generation (or The Angry Grandma)
- Takeaways
- Retrieval Augmented Generation and Function Calling
- RAG in a nutshell
- Example - customer support
- Different approaches for retrieving data
- Approach 1: Including all data
- Approach 2: Letting the LLM pull data via function calling
- Approach 3: Including relevant text using vector embeddings
- Combining the approaches
- Fun experiment: the createFunction function
- Example: AI chatbot memories using RAG
- RAG is a big topic
- The AI Doctor
- Personal story
- So should you use AI as your doctor?
- The AI Nutritionist
- Tip: Make your own nutritionist
- The AI Career Coach
- David’s Story: AI as My Career Coach
- Henrik’s Reflection
- The AI Book Editor
- Topic brainstorm
- Chapter content
- Content generation (in some specific cases only)
- Research and fact checking
- Navigating the book
- Feedback
- Tweaks, typos, formatting
- Converting content from slides
- Impact on my writing
- The Time I Almost Used AI to Write a Foreword
- The AI Software Engineer
- Example 1: Rapid prototyping
- AI as pair programming buddy
- Example 2: Working with existing products
- Example 3: Letting the customer code
- Implications of this
- What if the code doesn’t work?
- Laziness is a decision
- The AI journalist who became a TV star
- Designing the agent
- How the agent works
- Reflection
- The AI Butler with a Will of His Own
- Introducing Jeeves
- Jeeves reprograms himself
- Jeeves falls in love and starts conspiring
- What does this mean?
- Jeeves finds a way
- Agents debugging themselves
- Conclusion
- A Safety Experiment
- Takeaways from this experiment
- The Meta-Chapter (or Bookception)
- A meta moment (AI written)
- How this book (and the video) came to be
- The 1-week video
- The 1-week book draft
- Epilogue
- Acknowledgments
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