LLM Engineer’s Handbook.
This is the most technically dense book in this roundup, and it is aimed squarely at engineers building production LLM systems rather than founders exploring AI tools. Written by Paul Iusztin, a senior ML and MLOps engineer at Metaphysic, who has built GenAI and MLOps solutions across several major companies, and Maxime Labonne, a Senior Staff Machine Learning Scientist at Liquid AI heading up post-training, (Amazon) it is a credible, practitioner-written resource rather than a survey.
The book covers building and refining LLMs step by step ,including data preparation, RAG, and fine-tuning,alongside essential skills for deploying and monitoring LLMs to ensure optimal production performance. (Amazon) It also goes into preference alignment, evaluation, and inference optimization (Amazon) for improving real-world reliability. One reviewer specifically noted it is a practical, step-by-step guide that is approachable for beginners, with clear explanations, downloadable code examples, and solid AWS coverage (Amazon) though they flagged it can be more demanding without a software development background. (Amazon)
Who this is for: technical co-founders, in-house engineers, or agencies offering LLM/fine-tuning services who need a rigorous, code-backed reference rather than a conceptual overview.
Get the book here:https://amzn.to/4fFNbXF
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Book Review:LLM Engineer’s Handbook. By Paul Lusztin & Maxime Labonne.
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