The inner workings of Large Language Models
The inner workings of Large Language Models
how neural networks learn language
About the Book
Large language models like ChatGPT, Claude, and Copilot are changing the way we work, learn, and build software — but how do they actually work?
If you've ever wondered what goes on under the hood of an LLM, this book is for you. It's written by a developer who decided to go deep — and take careful notes along the way.
This book explains how large language models operate, step by step, using clear language and practical illustrations. Whether you're a software developer, tech enthusiast, or simply curious, you'll come away with a strong intuitive understanding of how these models generate text, learn from data, and represent meaning using mathematics.
You'll learn about:
- Tokenization, embeddings, and vector representations
- The inner workings of self-attention and transformer layers
- How models are trained, fine-tuned, and optimized
- Common myths and misconceptions
- And much more — including how I used LLMs during my research for the book
This isn't an academic text or a deep dive into every research paper — it's a practical, visual, and approachable explanation for people who want to understand LLMs without getting lost in jargon.
If you're ready to move beyond just using AI and start understanding it, this book will give you a solid foundation
About the Author
Table of Contents
- About this book
- About the author
- Part I – Foundations of Language Models
- It is all about predicting the next word, one word at a time
- Tokenization: How text becomes numbers
- Embeddings: From tokens to vectors
- Byte pair encoding: Handling unknown words
- Training an LLM: What data is needed for training?
- Embedding lookup
- Position matrix
- Attention mechanisms
- Self-attention with trainable weights
- Causal attention
- Multi-Head Attention
- Layer normalization (layer norm)
- FeedForward (aka MLP)
- Transformer blocks and layers
- The full transformer block
- What is a logit, and how is it calculated?
- Picking the next token
- Part II – Making the model useful
- Time for pre-training
- Calculating loss and adjusting parameters
- Backpropagation
- Fine tuning
- Quick Reference table: How LLMs work in 8 steps
- What about reasoning models?
- Can I teach an LLM new things it doesn’t already know?
- Do we now understand how AI actually works
- Part III – Reflection and practical use
- Do LLMs really understand language?
- Why do LLMs make mistakes?
- Why LLMs will soon be so much more than a chatbot
- How I have been using LLMs during my research
- Final thoughts
- Resources for further learning
- Glossary
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