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Build Your First LLM

A Hands-On Guide to Language Models

Learn how large language models work by building one from scratch. This hands-on guide walks you from first principles to a working Transformer you understand inside out.

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About

About

About the Book

This book teaches you to build a Large Language Model from scratch. Not just use tools like ChatGPT, but understand them, construct them, make them work.

If you're curious about how AI actually works, this book is for you. We assume no prior programming experience and no advanced math. We start from first principles and build up, piece by piece, explaining everything along the way.

The book is organized as one continuous, hands-on project:

Part I: Foundations: What AI and LLMs actually are, how machines process language, and the innovations that made this possible.

Part II: Python Essentials: The programming tools you'll need, nothing more.

Part III: Build Your First LLM: The core of the book. Implement tokenization, embeddings, attention, and the full Transformer architecture from scratch.

Part IV: Make It Useful: Fine-tuning, prompt engineering, and building practical applications.

Part V: Share It With the World: Deploying your creation so others can use it.

Every concept is explained before it's coded. We use analogies to make abstract ideas concrete. Every line of code is justified. We don't skip steps, and we don't hand-wave.

Every chapter includes a companion notebook that runs in Google Colab (no installation, no setup on your computer, just click and start coding).

Each chapter ends with review questions to test your understanding, with all answers provided in Appendix D. Notebooks are also available on GitHub at https://github.com/FirstLLM/code.

Understanding comes through doing. The concepts will make sense because you'll build them yourself.

Author

About the Author

Hasan Degismez

Hasan Degismez is an engineering manager and senior software developer with more than 15 years of experience building backend systems and cloud infrastructure. His work spans high-traffic web applications, distributed services, and data-intensive platforms, with a focus on reliability, performance, and simple, understandable architectures.

He has worked extensively with PHP, Go, Node.js, MySQL, PostgreSQL, DynamoDB, Redis, AWS, Docker, Kubernetes, and Terraform, and has spent much of his career mentoring engineers and shaping engineering teams. Hasan previously published the Turkish edition of PHP Pandas and writes for readers who want clear explanations, practical examples, and code they can reason about.

Translations

Translations

Contents

Table of Contents

  • Preface
  • Part I: Foundations
    • Chapter 1: What is AI, Really?
    • Chapter 2: How Computers "Understand" Words
    • Chapter 3: The Attention Mechanism
    • Chapter 4: The Transformer Architecture
  • Part II: Python Essentials
    • Chapter 5: Your First Python Program
    • Chapter 6: NumPy & PyTorch Survival Guide
  • Part III: Build Your First LLM
    • Chapter 7: Preparing Your Data
    • Chapter 8: Building the Tokenizer
    • Chapter 9: The Embedding Layer
    • Chapter 10: Attention Is All You Need
    • Chapter 11: Building the Transformer
    • Chapter 12: Training Your Model
  • Part IV: Make It Useful
    • Chapter 13: Fine-Tuning Your Model
    • Chapter 14: Prompt Engineering
    • Chapter 15: Building Applications
  • Part V: Share It With the World
    • Chapter 16: Preparing for Production
    • Chapter 17: Deployment Options
    • Chapter 18: What's Next
  • Appendices
    • Appendix A: Troubleshooting Guide
    • Appendix B: Glossary
    • Appendix C: Math Refresher
    • Appendix D: Review Question Answers

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