LLM Engineers Handbook
by Paul Iusztin
Paul Iusztin's hands-on framework for building production-ready LLM systems, featuring the innovative LLM Twin concept for AI personalization.
"It moves beyond isolated Jupyter notebooks, focusing on how to build production-grade end-to-end LLM systems".
Editorial Summary
Paul Iusztin is a senior AI/ML engineer with over seven years of experience at companies like Metaphysic, CoreAI, and Continental, and founder of the Decoding ML educational platform. The LLM Engineer's Handbook moves beyond isolated Jupyter notebooks, focusing on how to build production-grade end-to-end large language model systems. The book centers on building an LLM Twin, an AI character that learns to write like a particular person by incorporating its style, voice, and personality into an LLM. Throughout this book, readers learn data engineering, supervised fine-tuning, and deployment using the hands-on LLM Twin use case. The guide explores cutting-edge advancements including inference optimization, preference alignment, and real-time data processing, making it a vital resource for applying large language models in production projects.
Perspective
"The LLM Engineer's Handbook treats building with large language models as an engineering discipline rather than a prompt-crafting art — Iusztin's production focus means you are learning how systems actually work at scale, not how to impress a demo audience. The distinctive contribution is the LLM Twin project as a through-line: a single complex use case that forces every architectural decision to be real rather than hypothetical. Engineers who have experimented with LLM APIs and are ready to build something that actually runs reliably in production will find this the most direct path forward."
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