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Mastering RAG for Al Chatbots: Build Intelligent Large Language Model Applications from Scratch to Production
Paperback
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Publisher: Independently Published
Pages: 388
Weight: 1.48
Height: 0.80 Width: 7.00 Depth: 10.00
Language: English
Build Intelligent Large Language Model Applications from Scratch to Production
In the rapidly evolving world of artificial intelligence, Retrieval-Augmented Generation (RAG) has become one of the most powerful techniques for building accurate, context-aware, and trustworthy AI applications. Mastering RAG for AI Chatbots is a practical, comprehensive guide designed to help developers, AI engineers, data scientists, and technology enthusiasts understand, build, and deploy production-ready RAG systems with confidence.
From the fundamentals of large language models and vector databases to advanced retrieval strategies, hybrid search techniques, reranking methods, and agentic workflows, this book provides a step-by-step journey through the complete RAG development lifecycle. Readers will learn how to connect AI models with proprietary knowledge sources, improve response accuracy, reduce hallucinations, and create intelligent chatbot solutions capable of delivering reliable answers in real-world environments.
Through clear explanations, hands-on examples, architectural diagrams, and practical code implementations, you'll discover how to design scalable RAG pipelines, optimize retrieval performance, evaluate system quality, and deploy enterprise-grade applications. The book also explores modern tools, frameworks, and emerging innovations that are shaping the future of AI-powered knowledge systems.
Whether you're building customer support assistants, enterprise knowledge bots, research assistants, educational platforms, or domain-specific AI applications, this guide equips you with the skills, strategies, and best practices needed to transform raw data into intelligent conversational experiences.
Inside This Book You'll Learn:
- Core concepts behind Retrieval-Augmented Generation (RAG)
- Vector embeddings, semantic search, and vector databases
- Document ingestion, chunking, indexing, and retrieval optimization
- Hybrid search, reranking, and advanced retrieval techniques
- Building conversational AI systems with memory and context awareness
- Integrating RAG with modern LLM frameworks and APIs
- Evaluation, monitoring, security, and governance best practices
- Scaling RAG applications from prototype to production
- Agentic AI workflows and the future of intelligent knowledge systems
Whether you're just starting your AI journey or looking to advance your expertise, Mastering RAG for AI Chatbots provides the knowledge and practical skills required to build the next generation of intelligent AI applications.
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