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Advanced RAG Systems: A Practical Guide to Hybrid Retrieval, Reranking, GraphRAG, Agentic RAG and Evaluation
Paperback
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Publisher: Independently Published
Pages: 272
Weight: 0.96
Height: 0.57 Width: 6.69 Depth: 9.61
Language: English
Advanced RAG Systems provides a practical guide to engineering Retrieval-Augmented Generation systems beyond the basic vector-search-and-prompt approach.
You will learn how to design the complete RAG pipeline, from preparing and chunking source data to embeddings, indexing, retrieval, reranking, context construction, generation, evaluation, security, monitoring, and production deployment.The book explains how semantic vector search and BM25 keyword retrieval can work together through hybrid retrieval, helping your system handle both meaning-based questions and exact terms such as product names, identifiers, error codes, and technical references.
For more complex applications, the book explores GraphRAG for relationship-heavy and multi-hop questions, Agentic RAG for controlled multi-step retrieval, query routing across different data sources, and adaptive retrieval strategies that can respond when the first search does not provide enough evidence.
Inside this practical guide, you will learn how to:
- Build reliable RAG data and indexing pipelines
- Design semantic, keyword, and hybrid retrieval systems
- Combine and rank results using techniques such as Reciprocal Rank Fusion
- Apply cross-encoder and LLM-based reranking
- Select, compress, and manage retrieved context
- Build GraphRAG systems around entities and relationships
- Design controlled Agentic RAG workflows
- Implement multi-query and multi-hop retrieval
- Route queries across different knowledge sources
- Evaluate retrieval with Precision@K, Recall@K, MRR, NDCG, and other useful metrics
- Measure answer quality, faithfulness, grounding, and citation support
- Identify and correct common RAG failure patterns
- Protect RAG applications against prompt injection, unauthorized retrieval, and knowledge base poisoning
- Trace retrieval and generation for better observability
- Optimize latency, token usage, retrieval performance, and cost
- Scale retrieval, indexing, reranking, and model services
- Build and prepare a complete advanced RAG system for production
Rather than promoting one framework, vector database, or model provider, this book focuses on practical engineering principles that remain useful as individual technologies change.
Whether you are a developer, AI engineer, machine learning engineer, data engineer, software architect, or technical professional, Advanced RAG Systems will help you understand what separates a promising RAG prototype from a system designed for real applications.If you are ready to move beyond basic vector search and build RAG applications with stronger retrieval, better ranking, measurable quality, controlled agentic behavior, and production-ready architecture, this book gives you the practical foundation to do it.
Build RAG systems that do more than retrieve information. Build systems that retrieve the right evidence, use it effectively, and produce answers you can evaluate, improve, and trust.Get your copy of Advanced RAG Systems today and start building more capable, reliable, and production-ready RAG applications.
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