{"product_id":"9781836207030","title":"LLM Design Patterns : A Practical Guide to Building Robust and Efficient AI Systems","description":"\u003cp\u003eExplore reusable design patterns, including data-centric approaches, model development, model fine-tuning, and RAG for LLM application development and advanced prompting techniques\u003c\/p\u003e\n\n\u003cp\u003eFree with your book: PDF Copy, AI Assistant, and Next-Gen Reader\u003c\/p\u003e\n\n\u003cp\u003eKey Features\u003c\/p\u003e\n\n\u003cp\u003eLearn comprehensive LLM development, including data prep, training pipelines, and optimization\n\u003cbr\u003eExplore advanced prompting techniques, such as chain-of-thought, tree-of-thought, RAG, and AI agents\n\u003cbr\u003eImplement evaluation metrics, interpretability, and bias detection for fair, reliable models\u003c\/p\u003e\n\n\u003cp\u003eBook DescriptionThis practical guide for AI professionals enables you to build on the power of design patterns to develop robust, scalable, and efficient large language models (LLMs). Written by a global AI expert and popular author driving standards and innovation in Generative AI, security, and strategy, this book covers the end-to-end lifecycle of LLM development and introduces reusable architectural and engineering solutions to common challenges in data handling, model training, evaluation, and deployment.\n\u003cbr\u003eYou'll learn to clean, augment, and annotate large-scale datasets, architect modular training pipelines, and optimize models using hyperparameter tuning, pruning, and quantization. The chapters help you explore regularization, checkpointing, fine-tuning, and advanced prompting methods, such as reason-and-act, as well as implement reflection, multi-step reasoning, and tool use for intelligent task completion. The book also highlights Retrieval-Augmented Generation (RAG), graph-based retrieval, interpretability, fairness, and RLHF, culminating in the creation of agentic LLM systems.\n\u003cbr\u003eBy the end of this book, you'll be equipped with the knowledge and tools to build next-generation LLMs that are adaptable, efficient, safe, and aligned with human values.\n\u003cbr\u003eWhat you will learn\u003c\/p\u003e\n\n\u003cp\u003eImplement efficient data prep techniques, including cleaning and augmentation\n\u003cbr\u003eDesign scalable training pipelines with tuning, regularization, and checkpointing\n\u003cbr\u003eOptimize LLMs via pruning, quantization, and fine-tuning\n\u003cbr\u003eEvaluate models with metrics, cross-validation, and interpretability\n\u003cbr\u003eUnderstand fairness and detect bias in outputs\n\u003cbr\u003eDevelop RLHF strategies to build secure, agentic AI systems\u003c\/p\u003e\n\n\u003cp\u003eWho this book is forThis book is essential for AI engineers, architects, data scientists, and software engineers responsible for developing and deploying AI systems powered by large language models. A basic understanding of machine learning concepts and experience in Python programming is a must.\u003c\/p\u003e","brand":"Packt Publishing Limited","offers":[{"title":"Default Title","offer_id":48522529734891,"sku":"EB_CP1000_F05-01_SIMS","price":95.2,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0758\/4484\/5803\/files\/9781836207030-1.jpg?v=1787999264","url":"https:\/\/kinokuniya.com.sg\/products\/9781836207030","provider":"Books Kinokuniya Singapore","version":"1.0","type":"link"}