{"product_id":"llm-design-patterns-a-practical-guide-to-building-robust-and-efficient-ai-systems-paperback","title":"LLM Design Patterns: A Practical Guide to Building Robust and Efficient AI Systems - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eKen Huang\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eExplore reusable design patterns, including data-centric approaches, model development, model fine-tuning, and RAG for LLM application development and advanced prompting techniques\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Learn comprehensive LLM development, including data prep, training pipelines, and optimization\u003c\/p\u003e\u003cp\u003e- Explore advanced prompting techniques, such as chain-of-thought, tree-of-thought, RAG, and AI agents\u003c\/p\u003e\u003cp\u003e- Implement evaluation metrics, interpretability, and bias detection for fair, reliable models\u003c\/p\u003e\u003cp\u003e- Print or Kindle purchase includes a free PDF eBook\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThis 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\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\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\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Implement efficient data prep techniques, including cleaning and augmentation\u003c\/p\u003e\u003cp\u003e- Design scalable training pipelines with tuning, regularization, and checkpointing\u003c\/p\u003e\u003cp\u003e- Optimize LLMs via pruning, quantization, and fine-tuning\u003c\/p\u003e\u003cp\u003e- Evaluate models with metrics, cross-validation, and interpretability\u003c\/p\u003e\u003cp\u003e- Understand fairness and detect bias in outputs\u003c\/p\u003e\u003cp\u003e- Develop RLHF strategies to build secure, agentic AI systems\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThis 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\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Introduction to LLM Design Patterns\u003c\/p\u003e\u003cp\u003e- Data Cleaning for LLM Training\u003c\/p\u003e\u003cp\u003e- Data Augmentation\u003c\/p\u003e\u003cp\u003e- Handling Large Datasets for LLM Training\u003c\/p\u003e\u003cp\u003e- Data Versioning\u003c\/p\u003e\u003cp\u003e- Dataset Annotation and Labeling\u003c\/p\u003e\u003cp\u003e- Training Pipeline\u003c\/p\u003e\u003cp\u003e- Hyperparameter Tuning\u003c\/p\u003e\u003cp\u003e- Regularization\u003c\/p\u003e\u003cp\u003e- Checkpointing and Recovery\u003c\/p\u003e\u003cp\u003e- Fine-Tuning\u003c\/p\u003e\u003cp\u003e- Model Pruning\u003c\/p\u003e\u003cp\u003e- Quantization\u003c\/p\u003e\u003cp\u003e- Evaluation Metrics\u003c\/p\u003e\u003cp\u003e- Cross-Validation\u003c\/p\u003e\u003cp\u003e- Interpretability\u003c\/p\u003e\u003cp\u003e- Fairness and Bias Detection\u003c\/p\u003e\u003cp\u003e- Adversarial Robustness\u003c\/p\u003e\u003cp\u003e- Reinforcement Learning from Human Feedback\u003c\/p\u003e\u003cp\u003e- Chain-of-Thought Prompting\u003c\/p\u003e\u003cp\u003e- Tree-of-Thoughts Prompting\u003c\/p\u003e\u003cp\u003e- Reasoning and Acting\u003c\/p\u003e\u003cp\u003e- Reasoning WithOut Observation\u003c\/p\u003e\u003cp\u003e- Reflection Techniques\u003c\/p\u003e\u003cp\u003e- Automatic Multi-Step Reasoning and Tool Use\u003c\/p\u003e\u003cp\u003e- Retrieval-Augmented Generation\u003c\/p\u003e\u003cp\u003e- Graph-Based RAG\u003c\/p\u003e\u003cp\u003e- Advanced RAG\u003c\/p\u003e\u003cp\u003e- Evaluating RAG Systems\u003c\/p\u003e\u003cp\u003e- Agentic Patterns\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 534\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.08 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e May 30, 2025\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":44847156985958,"sku":"9781836207030","price":89.35,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0599\/7255\/0758\/files\/b-O4j1_ek-9781836207030.webp?v=1772708724","url":"https:\/\/infinitylightwa.com\/products\/llm-design-patterns-a-practical-guide-to-building-robust-and-efficient-ai-systems-paperback","provider":"Infinity Light","version":"1.0","type":"link"}