Mastering RAG Programming : Unlocking the Power Behind AI Assistants for Smarter Search and Intelligent Responses
Overview
Discover the secrets behind today's smartest AI assistants-and learn how to build them yourself. Mastering RAG Programming is your complete guide to Retrieval-Augmented Generation (RAG), the cutting-edge AI technique that combines search with natural language generation to deliver accurate, context-aware answers. This book will help you design, build, and deploy advanced RAG systems that power modern chatbots, virtual assistants, and intelligent search engines. Inside this book, you'll learn: Core Concepts Made Simple: Understand how retrieval and generation work together to create superior AI responses. Architecture and Design: Explore dense retrieval, reranking, vector databases, and generative models with easy-to-follow diagrams. Hands-On Coding: Step-by-step examples using popular libraries like PyTorch, Hugging Face, and LangChain. Production-Ready Techniques: Optimize for speed, accuracy, and scalability while managing cost and complexity. Real-World Applications: Build chatbots, knowledge bases, and semantic search engines that truly understand user needs. Ethical AI and Safety: Learn strategies to reduce bias, control hallucinations, and ensure responsible deployment. Clear, practical, and up-to-date, Mastering RAG Programming is the ideal resource for AI engineers, software developers, data scientists, and anyone who wants to create smarter, more reliable AI systems. Perfect for:
✓AI/ML practitioners looking to level up their skills
✓Developers building next-generation search and chat applications
✓Researchers and students exploring state-of-the-art NLP
✓Anyone who wants to understand the technology powering intelligent assistants Bring your AI projects to life. Learn to master RAG today.
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Details
- ISBN-13: 9798291116838
- ISBN-10: 9798291116838
- Publisher: Independently Published
- Publish Date: July 2025
- Dimensions: 9.21 x 6.14 x 0.26 inches
- Shipping Weight: 0.4 pounds
- Page Count: 122
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