Hybrid RAG Foundations: Knowledge Graphs, n8n & GPT for Reliable Automations: A Practical Guide to Building Trustworthy AI Workflows

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.