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
"Essential reading for AI engineers and machine learning practitioners building production LLM systems in the post-ChatGPT era. This practical guide fills the critical gap between academic LLM theory and real-world deployment challenges that teams at companies using OpenAI, Anthropic, and open-source models face daily."
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