Large Language Models from First Principles: A Hands-On University Course

Understand how large language models work. Build your own. Explore the future of artificial intelligence.

Large language models (LLMs) are transforming how we interact with information, develop software, and solve complex problems. But how do these models actually work? What happens inside a Transformer? How are models trained, fine-tuned, evaluated, and deployed?

Large Language Models from First Principles: A Hands-On University Course takes you beyond simply using AI tools and introduces you to the fundamental principles, architectures, and engineering techniques behind modern language models.

What You Will Learn

Build a Strong Foundation
Understand neural networks, tokenization, embeddings, positional information, and the mathematical foundations of language modeling.

Explore Transformer Architecture
Learn how self-attention, causal masking, multi-head attention, normalization, feed-forward networks, and residual connections work together to power modern language models.

Build and Train a GPT-Style Model
Discover how to implement a decoder-only Transformer, prepare training data, optimize model parameters, monitor training, and generate text.

Understand Model Scaling and Optimization
Explore scaling laws, mixed-precision training, gradient accumulation, memory optimization, and distributed training concepts.

Fine-Tune and Adapt Pretrained Models
Learn supervised fine-tuning, parameter-efficient techniques such as LoRA and QLoRA, instruction tuning, and introductory approaches to aligning language models with human preferences.

Develop Retrieval-Augmented Generation Applications
Build an understanding of embeddings, vector search, document retrieval, and context-grounded generation, with practical applications such as a campus question-answering assistant.

Evaluate and Deploy LLMs
Explore model evaluation, inference optimization, efficient deployment, and the challenges of building reliable language-model applications.

Understand Responsible AI Engineering
Examine privacy, bias, safety, licensing, red teaming, and governance considerations involved in developing and deploying LLM systems.

Who Is This Book For?

This book is particularly suitable for:

• Undergraduate and postgraduate students in computer science, artificial intelligence, and data science.

• University instructors seeking a structured, practical resource for teaching LLM engineering.

• Researchers who want to strengthen their understanding of Transformer-based language models.

• Software developers and aspiring AI engineers interested in building and adapting LLMs.

• Professionals seeking a systematic introduction to the technologies behind modern generative AI.

Basic familiarity with programming and introductory machine learning concepts will be helpful. The book builds its core concepts progressively, making the learning journey accessible while maintaining academic rigor.

From Theory to Practice

Rather than treating large language models as black boxes, this book encourages readers to understand the principles behind them, implement essential components, experiment with training and adaptation, and critically evaluate their applications.

By connecting foundational theory with practical implementation, it provides a pathway from understanding how language models work to developing, evaluating, and responsibly applying LLM-powered systems.

Whether you are learning LLM engineering for the first time or teaching the next generation of AI practitioners, this book offers a structured foundation for understanding and building modern language models.