BUILDING LARGE LANGUAGE MODELS

Building Large Language Models

Design, Training, Scaling, and Production Deployment for High-Performance LLM Systems

Large Language Models are no longer research experiments — they are production-critical systems powering search, assistants, automation, analytics, and autonomous agents. But most resources stop at theory or toy examples.

This book goes where real-world LLM engineering begins.

Written by senior AI engineer Finn Tech, Building Large Language Models is a deep, production-level guide for developers, ML engineers, and data scientists who want to design, train, scale, optimize, and deploy LLM systems that actually work in production.

This is not an introductory NLP book. It is a hands-on engineering manual for building robust, scalable, and secure LLM pipelines from the ground up.

What you’ll learn inside this book

How modern LLM architectures are designed and why they scale

Practical approaches to data engineering, training pipelines, and fine-tuning

How to design high-performance inference and deployment architectures

Production-ready workflows using Python, LangChain, LangGraph, and agentic systems

Techniques for debugging hallucinations, instability, and failure modes

Scaling strategies including distributed training, optimization, and cost control

How to monitor, test, and secure LLM systems in real-world environments

Enterprise-grade considerations for compliance, governance, and ethical AI

Every chapter blends theory, implementation, and expert insight, with complete Python examples and real production scenarios — not toy demos.

Who this book is for

Experienced software developers working with AI systems

Machine learning engineers building large-scale models

Data scientists and applied researchers moving from experiments to production

Technical founders and architects designing LLM-powered platforms

If you already understand transformers and want to build serious LLM systems, this book was written for you.

Why this book stands out

Focuses on production deployment, not just research

Treats LLMs as engineering systems, not black boxes

Emphasizes scalability, reliability, and optimization

Written with the depth of a professional AI textbook, not a blog tutorial