Overview

Hybrid RAG Foundations equips practitioners with the practical know-how to build AI automations that are not only fluent, but verifiable. From the fundamentals of Retrieval-Augmented Generation to the discipline of knowledge-graph modeling, this book walks beginners and intermediate engineers through step-by-step patterns for creating trustworthy workflows using knowledge graphs, n8n orchestration, and GPT-class models. You'll learn how to: design RAG pipelines that reduce hallucination and factual drift; combine vector search with structured KGs for deterministic answers; orchestrate ingestion, retrieval, and LLM synthesis in n8n; validate outputs with citation checks, confidence scoring, and human-in-the-loop gates. Packed with engineering blueprints, testing strategies, and real-world examples (customer support, compliance, research assistants), this practical guide focuses on reliability and governance as first principles-not afterthoughts. Whether you're building your first automation or hardening production systems, this book gives you the patterns, templates, and operational checklists to ship AI workflows that scale-and that stakeholders can trust.

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