LLM Primer I How Generative AI Works A Clear and Practical Guide to the Foundations of Large Language Models

This book is different.
LLM Primer is a clear, technically grounded guide for engineers, technical product managers, advanced beginners, and curious professionals who want to truly understand how large language models work - not just how to use them.
You will not find hype, vague metaphors, or hand-waving explanations. Instead, you will build real understanding from first principles.
Starting with probability and tokens, the book gradually guides you through:How text becomes numerical representationsWhy next-token prediction worksThe evolution from n-gram models to neural networksThe Transformer architecture and self-attentionTraining at scale: data, optimization, and infrastructureFine-tuning and instruction tuningEmbeddings, retrieval, and RAG systemsPerformance, scaling, and cost tradeoffsSafety, hallucinations, bias, and governanceDesigning production-grade LLM systemsEvery concept is introduced intuitively first, then explored more deeply. Technical terms are clearly defined. Examples are concrete and practical. Mathematical ideas are explained in plain language before any formal detail appears.
This book assumes you know basic programming, but not deep learning. By the end, you will be able to:Reason about how LLMs actually generate textUnderstand why scaling changes behaviorEvaluate model performance using entropy and perplexityDesign better prompts and fine-tuning strategiesMake informed architectural decisions in real-world systemsIf you work with AI systems - or plan to - understanding the foundations is no longer optional. It is a professional advantage.
LLM Primer gives you that foundation.
Clear. Practical. Technically honest.
Not just how to use large language models - but how they truly work.

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