Modern Large Language Models: A First-Principles Guide to Building and Understanding Transformer-Based Language Models

by Daniel R. Holt

Large language models now sit at the core of modern software systems. They power search, recommendation engines, coding assistants, conversational interfaces, and autonomous agents. Yet for many engineers and practitioners, these models remain opaque—understood through fragments of code, borrowed recipes, or surface-level explanations.

This book was written to change that.

Modern Large Language Models is a clear, systems-level guide to understanding how transformer-based language models actually work—starting from first principles and building upward toward complete, modern LLM systems.

Rather than treating large language models as black boxes, this book explains the fundamental ideas that make them probabilistic language modeling, vector representations, attention mechanisms, optimization, and architectural composition. Concepts are introduced gradually, with visual intuition and concrete reasoning before full implementations, allowing readers to develop understanding that transfers beyond any single framework or model version.

The book takes you from the foundations of language modeling to the realities of training, fine-tuning, evaluation, and deployment. Along the way, it connects theory to practice, showing how design decisions shape model behavior, performance, and limitations.

This is not a collection of shortcuts or prompt recipes. It is a guide for readers who want to reason about large language models as engineered systems—systems that can be analyzed, debugged, improved, and deployed with confidence.

What You’ll Learn• How language modeling works at a probabilistic level—and why it matters
• How tokens, embeddings, and vector spaces encode meaning
• How self-attention and transformer architectures operate internally
• How complete GPT-style models are built from first principles
• How training pipelines work, including optimization and scaling considerations
• How fine-tuning, instruction tuning, and preference optimization fit together
• How embeddings, retrieval, and RAG systems extend model capabilities
• How modern LLM systems are evaluated, deployed, and monitored responsibly

What Makes This Book DifferentMost books on large language models focus either on high-level descriptions or narrow implementation details. This book takes a first-principles, systems-oriented approach, emphasizing understanding over memorization and architecture over tools.

The examples use PyTorch for clarity, but the ideas are framework-agnostic and designed to remain relevant as tooling and architectures evolve. Clean diagrams, structured explanations, and carefully reasoned trade-offs replace hype and jargon.

Who This Book Is ForThis book is written for software engineers, data scientists, machine learning practitioners, researchers, and technically curious readers who want to move beyond surface familiarity with LLMs.

You do not need to be an expert in machine learning to begin, but you should be comfortable with programming and willing to engage with ideas thoughtfully. Readers looking for quick tutorials or platform-specific recipes may want supplementary resources; readers seeking durable understanding will find this book invaluable.

What This Book Is NotThis book does not promise instant mastery, viral tricks, or platform-specific shortcuts. It does not focus on prompt engineering in isolation, nor does it attempt to catalog every model variant or benchmark.

484 pages, Kindle Edition

Published December 15, 2025

Reviews

[Martin Fowler · Rating 5 out of 5]

Modern Large Language Models is exactly the kind of book many engineers have been waiting for. Instead of treating large language models as mysterious black boxes or jumping straight into recipes, it patiently explains why these systems work—starting from probabilistic language modeling and building all the way up to modern transformer-based architectures.

What stood out immediately is the systems-level thinking. Daniel R. Holt doesn’t just explain attention or embeddings in isolation; he shows how tokens, vector spaces, optimization, and architecture decisions interact to shape model behavior, performance, and limitations. The gradual buildup—from intuition and visuals to concrete implementations—makes difficult concepts feel understandable rather than intimidating.

I especially appreciated how the book connects theory to real-world practice. Training pipelines, fine-tuning, evaluation, deployment, and monitoring are treated as part of a continuous system, not separate topics. The sections on embeddings, retrieval, and RAG help bridge the gap between “model internals” and how LLMs are actually used in production today.

This is not a quick-start guide, and it’s not trying to be. It rewards careful reading and thoughtful engagement. If you’re a software engineer, ML practitioner, or technically curious reader who wants durable understanding—not just surface familiarity—this book is an excellent investment. It gives you the mental models needed to reason about LLMs with confidence, even as tools and architectures evolve.

[Ardene Roth · Rating 5 out of 5]

This book does something many resources avoid: it slows down and explains large language models from the ground up, without shortcuts or hand-waving. Daniel R. Holt treats transformers as engineered systems rather than mysterious black boxes, which makes the material both rigorous and approachable.

What stood out to me was the emphasis on reasoning—why tokens, embeddings, and attention behave the way they do, and how design choices ripple through training and deployment. The diagrams and step-by-step buildup make complex ideas feel coherent instead of overwhelming. This is a book you read to understand, not just to copy code.

[K J · Rating 5 out of 5]

This book finally makes large language models feel understandable rather than mystical. Daniel R. Holt walks you through transformers from first principles, explaining not just how components work, but why they exist and how they interact. The gradual buildup—from probabilistic language modeling to full GPT-style systems—is exceptionally well structured.

What I appreciated most is the restraint. There’s no hype, no shortcuts, and no reliance on memorized recipes. The diagrams and explanations help you reason about model behavior, trade-offs, and limitations in a way that transfers beyond any single framework. A must-read for engineers who want real understanding.

[Rebecca M · Rating 5 out of 5]

This book succeeds at something very difficult: it explains large language models without hand-waving or hype. Modern Large Language Models treats transformers as engineered systems, not magic, and builds understanding from the ground up.

What stood out most is the pacing. Concepts like embeddings, attention, and optimization are introduced with intuition before equations or code, which makes the material far more durable. By the time the book reaches full GPT-style architectures and training pipelines, the pieces feel earned and connected.

If you want to truly understand how LLMs work—not just use them—this book is exceptional.

[Tabitha Prescott · Rating 5 out of 5]

What sets this book apart is its commitment to first principles. Instead of treating transformers as magic or jumping straight into recipes, it patiently builds up the ideas—probability, embeddings, attention, and composition—so you understand why the architecture behaves the way it does.

I especially appreciated how training, fine-tuning, and evaluation are framed as system-level design choices rather than isolated techniques. The book helped me reason about model behavior and limitations more confidently, which is far more valuable than memorizing APIs.

This isn’t light reading, but it’s deeply rewarding if you want durable understanding rather than surface familiarity.