The inner workings of Large Language Models: how neural networks learn language

Understand ChatGPT, Claude, and Copilot beyond the buzzwordsLarge language models are transforming software development, productivity, and how we interact with computers.But most people only know how to use them, not how they actually work.If you have ever wondered:How does ChatGPT know what to say next?What are tokens and embeddings really doing?Why does attention matter so much?What happens inside a transformer layer?This book will make it click.A practical, visual, developer-friendly explanationThis is not a dense academic textbook. It is a clear, step by step walkthrough that explains complex ideas in plain language, supported by practical illustrations and simple examples.You will gain a strong intuitive understanding of how modern large language models generate text, learn from data, and represent meaning using mathematics.Inside you will learn:Tokenization and why you are billed per tokenEmbeddings and vector representations and how meaning becomes mathSelf-attention, causal masking, and multi-head attentionWhat actually happens inside a transformer blockLogits, softmax, decoding, sampling, and temperatureHow models are trained, fine tuned, and optimizedWhy hallucinations happen and what models really understandCommon myths and misconceptions about LLMsHow the author used LLMs while researching and writing this bookWho this book is forSoftware developers and engineersTech professionals and product buildersCurious learners who want understanding without heavy jargonAnyone using AI tools and wanting to know what is happening under the hoodMove beyond using AI and start understanding itYou do not need a math background. You do not need to read research papers.You just need curiosity.This book gives you a clear, practical foundation for understanding how large language models work, written to be approachable, visual, and genuinely enjoyable to read. Read more