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Agentic RAG with n8n: Build Practical AI Workflows with Retrieval, Reranking, and Chunking is your definitive, hands-on guide to creating intelligent automation systems that think, reason, and act.
In a world where most “AI tutorials” stop at theory, this book goes further-showing you exactly how to design real-world, agentic Retrieval-Augmented Generation (RAG) systems using n8n, vector databases, LLMs, and modern workflow engineering.
You’ll discover how to integrate retrieval, agentic chunking, reranking, and evaluation loops into seamless workflows that connect cloud APIs, local models (via Ollama), and structured data sources. Every concept is explained clearly and backed by complete, working examples-making this book perfect for both AI practitioners and beginners seeking a practical path into intelligent automation.
Unlike many AI books that recycle generic content, Agentic RAG with n8n delivers verified, executable workflows. Each chapter distills complex ideas into concise, actionable insights you can apply immediately-whether you’re building internal knowledge assistants, compliance copilots, or adaptive research agents.
Why This Book Stands Out
Action-first approach – every workflow, function, and command is complete, working, and explained in context.
Agentic Intelligence simplified – learn how agents reason dynamically and interact with your data.
n8n for automation mastery – build pipelines that blend AI reasoning, retrieval, and logic nodes without coding complexity.
From local to cloud – run lightweight models via Ollama or deploy scalable RAG systems using Neon, Supabase, or Qdrant.
Future-ready knowledge – updated for 2025 AI trends in context engineering, RAG evaluation, and GraphRAG integration.
About the AuthorNathan Steele is a technology author and workflow engineer specializing in AI systems design, automation, and context engineering. With years of hands-on experience building agentic frameworks for AI developers and enterprises, he combines deep technical knowledge with a focus on clarity, practicality, and reproducibility. His works empower readers to build, not just understand, cutting-edge AI and automation solutions.
Inside the Book (Table of Contents Highlights)
Chapter 1: Introduction to Agentic RAG and n8n
Chapter 2: Setting Up Your Environment
Chapter 3: Fundamentals of Retrieval-Augmented Generation
Chapter 4: Agentic Chunking in Practice
Chapter 5: Agentic RAG Workflows
Chapter 6: Reranking for Better Answers
Chapter 7: Hybrid Search and GraphRAG
Chapter 8: Evaluation and Monitoring
Chapter 9: Safety and Governance
Chapter 10: Deployment and Operations
Chapter 11: Internal Docs Copilot
Chapter 12: Research Digest Agent
Chapter 13: Customer Support Inbox Agent
Appendices: Quick Guides, Evaluation Metrics, and Local Demo Setup
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