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Synopsis
Building Intelligent AI Systems: Retrieval-Augmented Generation in Python
Overview
Modern AI systems require more than just deep learning—they need efficient retrieval and augmentation techniques to enhance their reasoning, accuracy, and adaptability. Building Intelligent AI Systems: Retrieval-Augmented Generation in Python is a comprehensive guide to implementing Retrieval-Augmented Generation (RAG) using Python. This book breaks down the core principles, practical applications, and hands-on implementation strategies that will help you build scalable and intelligent AI solutions.
By the end of this book, you will have a strong foundation in RAG, understand how to integrate external knowledge into AI workflows, and deploy production-ready retrieval-augmented models for real-world applications.
RAG is transforming AI by combining retrieval-based search with generative language models, improving performance across diverse domains such as chatbots, search engines, document summarization, and knowledge management. This book takes a practical approach, guiding you through setting up RAG pipelines, leveraging powerful libraries like LangChain and Haystack, optimizing retrieval mechanisms, and deploying efficient AI systems.
Whether you're a beginner looking to grasp the fundamentals or an experienced developer aiming to optimize AI workflows, this book provides the step-by-step guidance you need to master RAG in Python.
Key Features of This BookStep-by-Step Tutorials: Learn to build RAG pipelines from scratch using Python.
Real-World Applications: Implement AI-driven search, question answering, and intelligent assistants.
Optimized Retrieval Techniques: Improve AI accuracy using vector databases, embeddings, and ranking algorithms.
Hands-On Coding Examples: Get fully functional Python scripts for immediate implementation.
Deployment Strategies: Learn how to scale and deploy RAG systems in production environments.
AI and ML Engineers: Professionals looking to enhance AI models with external knowledge.
Data Scientists: Researchers and practitioners working on search and NLP applications.
Software Developers: Engineers interested in building intelligent search and chatbot solutions.
Tech Enthusiasts & Students: Anyone eager to explore the future of AI-powered retrieval systems.
Unlock the power of Retrieval-Augmented Generation (RAG) and build intelligent AI systems today! Grab your copy of Building Intelligent AI Systems: Retrieval-Augmented Generation in Python and take your AI skills to the next level.
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Paperback. Etat : new. Paperback. Building Intelligent AI Systems: Retrieval-Augmented Generation in PythonOverviewModern AI systems require more than just deep learning-they need efficient retrieval and augmentation techniques to enhance their reasoning, accuracy, and adaptability. Building Intelligent AI Systems: Retrieval-Augmented Generation in Python is a comprehensive guide to implementing Retrieval-Augmented Generation (RAG) using Python. This book breaks down the core principles, practical applications, and hands-on implementation strategies that will help you build scalable and intelligent AI solutions.By the end of this book, you will have a strong foundation in RAG, understand how to integrate external knowledge into AI workflows, and deploy production-ready retrieval-augmented models for real-world applications.RAG is transforming AI by combining retrieval-based search with generative language models, improving performance across diverse domains such as chatbots, search engines, document summarization, and knowledge management. This book takes a practical approach, guiding you through setting up RAG pipelines, leveraging powerful libraries like LangChain and Haystack, optimizing retrieval mechanisms, and deploying efficient AI systems.Whether you're a beginner looking to grasp the fundamentals or an experienced developer aiming to optimize AI workflows, this book provides the step-by-step guidance you need to master RAG in Python.Key Features of This BookStep-by-Step Tutorials: Learn to build RAG pipelines from scratch using Python.Real-World Applications: Implement AI-driven search, question answering, and intelligent assistants.Optimized Retrieval Techniques: Improve AI accuracy using vector databases, embeddings, and ranking algorithms.Hands-On Coding Examples: Get fully functional Python scripts for immediate implementation.Deployment Strategies: Learn how to scale and deploy RAG systems in production environments.Target AudienceAI and ML Engineers: Professionals looking to enhance AI models with external knowledge.Data Scientists: Researchers and practitioners working on search and NLP applications.Software Developers: Engineers interested in building intelligent search and chatbot solutions.Tech Enthusiasts & Students: Anyone eager to explore the future of AI-powered retrieval systems.Unlock the power of Retrieval-Augmented Generation (RAG) and build intelligent AI systems today! Grab your copy of Building Intelligent AI Systems: Retrieval-Augmented Generation in Python and take your AI skills to the next level. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798310818590
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Paperback. Etat : new. Paperback. Building Intelligent AI Systems: Retrieval-Augmented Generation in PythonOverviewModern AI systems require more than just deep learning-they need efficient retrieval and augmentation techniques to enhance their reasoning, accuracy, and adaptability. Building Intelligent AI Systems: Retrieval-Augmented Generation in Python is a comprehensive guide to implementing Retrieval-Augmented Generation (RAG) using Python. This book breaks down the core principles, practical applications, and hands-on implementation strategies that will help you build scalable and intelligent AI solutions.By the end of this book, you will have a strong foundation in RAG, understand how to integrate external knowledge into AI workflows, and deploy production-ready retrieval-augmented models for real-world applications.RAG is transforming AI by combining retrieval-based search with generative language models, improving performance across diverse domains such as chatbots, search engines, document summarization, and knowledge management. This book takes a practical approach, guiding you through setting up RAG pipelines, leveraging powerful libraries like LangChain and Haystack, optimizing retrieval mechanisms, and deploying efficient AI systems.Whether you're a beginner looking to grasp the fundamentals or an experienced developer aiming to optimize AI workflows, this book provides the step-by-step guidance you need to master RAG in Python.Key Features of This BookStep-by-Step Tutorials: Learn to build RAG pipelines from scratch using Python.Real-World Applications: Implement AI-driven search, question answering, and intelligent assistants.Optimized Retrieval Techniques: Improve AI accuracy using vector databases, embeddings, and ranking algorithms.Hands-On Coding Examples: Get fully functional Python scripts for immediate implementation.Deployment Strategies: Learn how to scale and deploy RAG systems in production environments.Target AudienceAI and ML Engineers: Professionals looking to enhance AI models with external knowledge.Data Scientists: Researchers and practitioners working on search and NLP applications.Software Developers: Engineers interested in building intelligent search and chatbot solutions.Tech Enthusiasts & Students: Anyone eager to explore the future of AI-powered retrieval systems.Unlock the power of Retrieval-Augmented Generation (RAG) and build intelligent AI systems today! Grab your copy of Building Intelligent AI Systems: Retrieval-Augmented Generation in Python and take your AI skills to the next level. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798310818590
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