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 ...
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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 Book Step-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 Audience 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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Add this copy of Building Intelligent AI Systems: Retrieval-Augmented to cart. $12.88, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2025 by Independently Published.
