Llm Application Design Patterns
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Llm Application Design Patterns
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Author : Peter Flemming
language : en
Publisher: Independently Published
Release Date : 2025-07-17
Llm Application Design Patterns written by Peter Flemming and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-17 with Computers categories.
What if you could build AI apps that think, reason, and adapt-just like human developers do? LLM Design Patterns isn't just another AI book-it's your ultimate playbook for building intelligent, production-ready applications using large language models. Whether you're creating a research assistant, a customer support agent, or a personalized tutor, this guide gives you the architectural blueprints, prompt strategies, and deployment workflows you need to build confidently with generative AI. Inside, you'll discover how to harness the full power of LLMs using proven design patterns-ranging from Retrieval-Augmented Generation (RAG) and agent loops to memory-aware reasoning and multimodal enhancements. You'll go beyond theory with hands-on, real-world projects, complete code examples, and step-by-step tutorials built with LangChain, OpenAI, LangServe, and FastAPI. Key benefits: Master prompt engineering through reusable patterns and templates Architect scalable, agentic AI systems that go beyond basic chatbots Add reasoning, memory, and even vision to your applications Deploy with confidence using modern frameworks like LangServe Learn by doing with full-stack projects and annotated codebases What makes this book truly unique? It's developer-first and battle-tested-blending expert commentary, practical insight, and modern best practices to help you go from idea to intelligent system without the guesswork. Whether you're an AI engineer, software developer, or product builder, this is the resource you've been waiting for to take your LLM skills to the next level. Ready to build smarter apps with generative AI? Start your journey today.
Designing Large Language Model Applications
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Author : Suhas Pai
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2025-03-06
Designing Large Language Model Applications written by Suhas Pai and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-06 with Computers categories.
Large language models (LLMs) have proven themselves to be powerful tools for solving a wide range of tasks, and enterprises have taken note. But transitioning from demos and prototypes to full-fledged applications can be difficult. This book helps close that gap, providing the tools, techniques, and playbooks that practitioners need to build useful products that incorporate the power of language models. Experienced ML researcher Suhas Pai offers practical advice on harnessing LLMs for your use cases and dealing with commonly observed failure modes. You’ll take a comprehensive deep dive into the ingredients that make up a language model, explore various techniques for customizing them such as fine-tuning, learn about application paradigms like RAG (retrieval-augmented generation) and agents, and more. Understand how to prepare datasets for training and fine-tuning Develop an intuition about the Transformer architecture and its variants Adapt pretrained language models to your own domain and use cases Learn effective techniques for fine-tuning, domain adaptation, and inference optimization Interface language models with external tools and data and integrate them into an existing software ecosystem
Genai On Aws
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Author : Olivier Bergeret
language : en
Publisher: John Wiley & Sons
Release Date : 2025-03-19
Genai On Aws written by Olivier Bergeret and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-19 with Computers categories.
The definitive guide to leveraging AWS for generative AI GenAI on AWS: A Practical Approach to Building Generative AI Applications on AWS is an essential guide for anyone looking to dive into the world of generative AI with the power of Amazon Web Services (AWS). Crafted by a team of experienced cloud and software engineers, this book offers a direct path to developing innovative AI applications. It lays down a hands-on roadmap filled with actionable strategies, enabling you to write secure, efficient, and reliable generative AI applications utilizing the latest AI capabilities on AWS. This comprehensive guide starts with the basics, making it accessible to both novices and seasoned professionals. You'll explore the history of artificial intelligence, understand the fundamentals of machine learning, and get acquainted with deep learning concepts. It also demonstrates how to harness AWS's extensive suite of generative AI tools effectively. Through practical examples and detailed explanations, the book empowers you to bring your generative AI projects to life on the AWS platform. In the book, you'll: Gain invaluable insights from practicing cloud and software engineers on developing cutting-edge generative AI applications using AWS Discover beginner-friendly introductions to AI and machine learning, coupled with advanced techniques for leveraging AWS's AI tools Learn from a resource that's ideal for a broad audience, from technical professionals like cloud engineers and software developers to non-technical business leaders looking to innovate with AI Whether you're a cloud engineer, software developer, business leader, or simply an AI enthusiast, Gen AI on AWS is your gateway to mastering generative AI development on AWS. Seize this opportunity for an enduring competitive advantage in the rapidly evolving field of AI. Embark on your journey to building practical, impactful AI applications by grabbing a copy today.
Interdependent Human Machine Teams
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Author : William Lawless
language : en
Publisher: Elsevier
Release Date : 2024-12-05
Interdependent Human Machine Teams written by William Lawless and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-05 with Computers categories.
Interdependent Human-Machine Teams: The Path to Autonomy examines the foundations, metrics, and applications of human-machine systems, the legal ramifications of autonomy, trust by the public, and trust by the users and AI systems of their users, integrating concepts from various disciplines such as AI, machine learning, social sciences, quantum mechanics, and systems engineering. In this book, world-class researchers, engineers, ethicists, and social scientists discuss what machines, humans, and systems should discuss with each other, to policymakers, and to the public.It establishes the meaning and operation of "shared contexts" between humans and machines, policy makers, and the public and explores how human-machine systems affect targeted audiences (researchers, machines, robots, users, regulators, etc.) and society, as well as future ecosystems composed of humans, machines, and systems. - Investigates how interdependence is the missing ingredient necessary to produce operational autonomous systems - Integrates concepts from a wide range of disciplines, including applied and theoretical AI, quantum mechanics, social sciences, and systems engineering - Presents debates, models, and concepts of mutual dependency for autonomous human-machine teams, challenging assumptions across AI, systems engineering, data science, and quantum mechanics
Ai Fundamentals Building Technical Knowledge From The Ground Up
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Author : Alan Knox
language : en
Publisher: Alan Knox
Release Date : 2025-07-11
Ai Fundamentals Building Technical Knowledge From The Ground Up written by Alan Knox and has been published by Alan Knox this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-11 with Computers categories.
Stop being intimidated by AI. Start understanding it. Artificial intelligence is transforming every industry, but you don't need a computer science degree to understand how it works. This book bridges the gap between AI hype and practical knowledge, giving business professionals the technical literacy to navigate the AI revolution confidently. What You'll Learn · How AI systems actually work, from algorithms to large language models · When and why to use different AI approaches for real problems · Practical implementation strategies, from data collection to deployment · Responsible AI development and ethical considerations · How to evaluate AI opportunities and manage AI projects successfully Perfect For Business executives, project managers, consultants, product managers, and entrepreneurs who need to make informed decisions about AI strategy and implementation. Your Competitive Advantage The most sophisticated AI in the world is worthless if you can't evaluate it, implement it, or use it effectively. This book provides the foundation for turning AI capabilities into business value. No technical background required. Just curiosity and willingness to learn.
Generative Ai Security
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Author : Ken Huang
language : en
Publisher: Springer Nature
Release Date : 2024-04-05
Generative Ai Security written by Ken Huang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-05 with Business & Economics categories.
This book explores the revolutionary intersection of Generative AI (GenAI) and cybersecurity. It presents a comprehensive guide that intertwines theories and practices, aiming to equip cybersecurity professionals, CISOs, AI researchers, developers, architects and college students with an understanding of GenAI’s profound impacts on cybersecurity. The scope of the book ranges from the foundations of GenAI, including underlying principles, advanced architectures, and cutting-edge research, to specific aspects of GenAI security such as data security, model security, application-level security, and the emerging fields of LLMOps and DevSecOps. It explores AI regulations around the globe, ethical considerations, the threat landscape, and privacy preservation. Further, it assesses the transformative potential of GenAI in reshaping the cybersecurity landscape, the ethical implications of using advanced models, and the innovative strategies required to secure GenAI applications. Lastly, the book presents an in-depth analysis of the security challenges and potential solutions specific to GenAI, and a forward-looking view of how it can redefine cybersecurity practices. By addressing these topics, it provides answers to questions on how to secure GenAI applications, as well as vital support with understanding and navigating the complex and ever-evolving regulatory environments, and how to build a resilient GenAI security program. The book offers actionable insights and hands-on resources for anyone engaged in the rapidly evolving world of GenAI and cybersecurity.
Llms In Enterprise
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Author : Ahmed Menshawy
language : en
Publisher:
Release Date : 2025-09-19
Llms In Enterprise written by Ahmed Menshawy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-19 with Computers categories.
Integrate large language models into your enterprise applications with advanced strategies that drive transformation Key Features Explore design patterns for applying LLMs to solve real-world enterprise problems Learn strategies for scaling and deploying LLMs in complex environments Get more relevant results and improve performance by fine-tuning and optimizing LLMs Purchase of the print or Kindle book includes a free PDF eBook Book Description The integration of large language models (LLMs) into enterprise applications is transforming how businesses use AI to drive smarter decisions and efficient operations. LLMs in Enterprise is your practical guide to bringing these capabilities into real-world business contexts. It demystifies the complexities of LLM deployment and provides a structured approach for enhancing decision-making and operational efficiency with AI.Starting with an introduction to the foundational concepts, the book swiftly moves on to hands-on applications focusing on real-world challenges and solutions. You'll master data strategies and explore design patterns that streamline the optimization and deployment of LLMs in enterprise environments. From fine-tuning techniques to advanced inferencing patterns, the book equips you with a toolkit for solving complex challenges and driving AI-led innovation in business processes.By the end of this book, you'll have a solid grasp of key LLM design patterns and how to apply them to enhance the performance and scalability of your generative AI solutions. What you will learn Apply design patterns to integrate LLMs into enterprise applications for efficiency and scalability Overcome common challenges in scaling and deploying LLMs Use fine-tuning techniques and RAG approaches to enhance LLM efficiency Stay ahead of the curve with insights into emerging trends and advancements, including multimodality Optimize LLM performance through customized contextual models, advanced inferencing engines, and evaluation patterns Ensure fairness, transparency, and accountability in AI applications Who this book is for This book is designed for a diverse group of professionals looking to understand and implement advanced design patterns for LLMs in their enterprise applications, including AI and ML researchers exploring practical applications of LLMs, data scientists and ML engineers designing and implementing large-scale GenAI solutions, enterprise architects and technical leaders who oversee the integration of AI technologies into business processes, and software developers creating scalable GenAI-powered applications.
Llm Design Patterns
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Author : Ken Huang
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-05-30
Llm Design Patterns written by Ken Huang and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-30 with Computers categories.
Explore reusable design patterns, including data-centric approaches, model development, model fine-tuning, and RAG for LLM application development and advanced prompting techniques Free with your book: PDF Copy, AI Assistant, and Next-Gen Reader Key Features Learn comprehensive LLM development, including data prep, training pipelines, and optimization Explore advanced prompting techniques, such as chain-of-thought, tree-of-thought, RAG, and AI agents Implement evaluation metrics, interpretability, and bias detection for fair, reliable models Book 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. You’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. By 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. What you will learn Implement efficient data prep techniques, including cleaning and augmentation Design scalable training pipelines with tuning, regularization, and checkpointing Optimize LLMs via pruning, quantization, and fine-tuning Evaluate models with metrics, cross-validation, and interpretability Understand fairness and detect bias in outputs Develop RLHF strategies to build secure, agentic AI systems Who this book is for This 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.
Machine Learning And Data Mining In Pattern Recognition
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Author :
language : en
Publisher:
Release Date : 2001
Machine Learning And Data Mining In Pattern Recognition written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Data mining categories.
Llms In Enterprise
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Author : Ahmed Menshawy
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-09-19
Llms In Enterprise written by Ahmed Menshawy and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-19 with Computers categories.
Integrate large language models into your enterprise applications with advanced strategies that drive transformation Key Features Explore design patterns for applying LLMs to solve real-world enterprise problems Learn strategies for scaling and deploying LLMs in complex environments Get more relevant results and improve performance by fine-tuning and optimizing LLMs Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe integration of large language models (LLMs) into enterprise applications is transforming how businesses use AI to drive smarter decisions and efficient operations. LLMs in Enterprise is your practical guide to bringing these capabilities into real-world business contexts. It demystifies the complexities of LLM deployment and provides a structured approach for enhancing decision-making and operational efficiency with AI. Starting with an introduction to the foundational concepts, the book swiftly moves on to hands-on applications focusing on real-world challenges and solutions. You’ll master data strategies and explore design patterns that streamline the optimization and deployment of LLMs in enterprise environments. From fine-tuning techniques to advanced inferencing patterns, the book equips you with a toolkit for solving complex challenges and driving AI-led innovation in business processes. By the end of this book, you’ll have a solid grasp of key LLM design patterns and how to apply them to enhance the performance and scalability of your generative AI solutions.What you will learn Apply design patterns to integrate LLMs into enterprise applications for efficiency and scalability Overcome common challenges in scaling and deploying LLMs Use fine-tuning techniques and RAG approaches to enhance LLM efficiency Stay ahead of the curve with insights into emerging trends and advancements, including multimodality Optimize LLM performance through customized contextual models, advanced inferencing engines, and evaluation patterns Ensure fairness, transparency, and accountability in AI applications Who this book is for This book is designed for a diverse group of professionals looking to understand and implement advanced design patterns for LLMs in their enterprise applications, including AI and ML researchers exploring practical applications of LLMs, data scientists and ML engineers designing and implementing large-scale GenAI solutions, enterprise architects and technical leaders who oversee the integration of AI technologies into business processes, and software developers creating scalable GenAI-powered applications.