The LLM Engineer’s Handbook is a comprehensive guide focused on teaching engineers how to build and deploy large language models (LLMs) in production environments. Here are the key aspects of the book:
Core Focus
The book uses a practical end-to-end project called the “LLM Twin” - an AI system designed to imitate someone’s writing style and personality - to demonstrate real-world LLM engineering concepts.
Key Topics Covered
- Data engineering and collection
- Supervised fine-tuning
- Model evaluation
- Inference optimization
- RAG (Retrieval-Augmented Generation) pipeline development
- MLOps principles and deployment
- LLMOps best practices
Book Structure
Chapters Overview
- Chapter 1: Introduces the LLM Twin concept and architecture
- Chapters 2-4: Cover tooling, data engineering, and RAG feature pipelines
- Chapters 5-7: Focus on model fine-tuning, preference alignment, and evaluation
- Chapters 8-10: Address inference optimization and deployment
- Chapter 11: Explores MLOps and LLMOps principles
Target Audience
The book is designed for:
- Software engineers transitioning into AI projects
- ML practitioners looking to implement LLM-based systems
- Technology professionals interested in practical LLM applications
Special Features
- Practical examples and hands-on implementation
- Focus on production-ready systems
- Integration of MLOps best practices
- Coverage of both fundamental concepts and advanced techniques
The book emphasizes building scalable, reproducible, and robust LLM applications while following industry best practices for deployment and maintenance in production environments.