LLM Engineer's Handbook: Master the art of engineering large language models from concept to production. Maxime Labonne, Paul Iusztin

LLM Engineer’s Handbook by Maxime Labonne and Paul Iusztin is a practical, expert-crafted guide for anyone looking to design, build, and deploy large language model–based applications with real-world impact. Written by two leading AI practitioners, this book bridges the gap between cutting-edge research and production-grade engineering.

From foundational principles to advanced deployment strategies, the book covers the full lifecycle of LLM projects. Readers will explore prompt engineering, fine-tuning, retrieval-augmented generation (RAG), model evaluation, and infrastructure design. It goes beyond code snippets, offering insights into architecture decisions, trade-offs, performance bottlenecks, and scaling challenges.

With a strong emphasis on hands-on implementation, best practices, and real-world patterns, the LLM Engineer’s Handbook empowers machine learning engineers, software developers, and technical product leads to bring LLMs from experimentation to production with confidence and clarity.

Whether you're integrating OpenAI or open-source models, working with vector databases, or optimizing inference pipelines, this book is your field manual for the modern era of generative AI development.