{"product_id":"llm-design-patterns-by-ken-huang","title":"LLM Design Patterns\nby Ken Huang","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\u003cbr\u003e\nLearn comprehensive LLM development, including data prep, training pipelines, and optimization\u003cbr\u003e\nExplore advanced prompting techniques, such as chain-of-thought, tree-of-thought, RAG, and AI agents\u003cbr\u003e\nImplement evaluation metrics, interpretability, and bias detection for fair, reliable models\u003cbr\u003e\nBook Description\u003cbr\u003e\nThis 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.\u003c\/p\u003e\n\n\u003cp\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.\u003c\/p\u003e\n\n\u003cp\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.\u003c\/p\u003e\n\n\u003cp\u003eWhat you will learn\u003cbr\u003e\nImplement efficient data prep techniques, including cleaning and augmentation\u003cbr\u003e\nDesign scalable training pipelines with tuning, regularization, and checkpointing\u003cbr\u003e\nOptimize LLMs via pruning, quantization, and fine-tuning\u003cbr\u003e\nEvaluate models with metrics, cross-validation, and interpretability\u003cbr\u003e\nUnderstand fairness and detect bias in outputs\u003cbr\u003e\nDevelop RLHF strategies to build secure, agentic AI systems\u003cbr\u003e\nWho this book is for\u003cbr\u003e\nThis 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\n\n\u003cp\u003eTable of Contents\u003cbr\u003e\nIntroduction to LLM Design Patterns\u003cbr\u003e\nData Cleaning for LLM Training\u003cbr\u003e\nData Augmentation\u003cbr\u003e\nHandling Large Datasets for LLM Training\u003cbr\u003e\nData Versioning\u003cbr\u003e\nDataset Annotation and Labeling\u003cbr\u003e\nTraining Pipeline\u003cbr\u003e\nHyperparameter Tuning\u003cbr\u003e\nRegularization\u003cbr\u003e\nCheckpointing and Recovery\u003cbr\u003e\nFine-Tuning\u003cbr\u003e\nModel Pruning\u003cbr\u003e\nQuantization\u003cbr\u003e\nEvaluation Metrics\u003cbr\u003e\nCross-Validation\u003cbr\u003e\nInterpretability\u003cbr\u003e\nFairness and Bias Detection\u003cbr\u003e\nAdversarial Robustness\u003cbr\u003e\nReinforcement Learning from Human Feedback\u003cbr\u003e\nChain-of-Thought Prompting\u003cbr\u003e\nTree-of-Thoughts Prompting\u003cbr\u003e\nReasoning and Acting\u003cbr\u003e\nReasoning WithOut Observation\u003cbr\u003e\nReflection Techniques\u003cbr\u003e\nAutomatic Multi-Step Reasoning and Tool Use\u003cbr\u003e\nRetrieval-Augmented Generation\u003cbr\u003e\nGraph-Based RAG\u003cbr\u003e\nAdvanced RAG\u003cbr\u003e\nEvaluating RAG Systems\u003cbr\u003e\nAgentic Patterns\u003c\/p\u003e","brand":"Books Feri","offers":[{"title":"Premium Paperback","offer_id":45409793769663,"sku":null,"price":370.0,"currency_code":"BDT","in_stock":true},{"title":"Premium Hardcover","offer_id":45405208248511,"sku":null,"price":470.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0692\/8148\/0895\/files\/416TihrbGVL._SX342_SY445_FMwebp.webp?v=1772989033","url":"https:\/\/booksferi.store\/products\/llm-design-patterns-by-ken-huang","provider":"Books Feri","version":"1.0","type":"link"}