Introducing RAG and Knowledge Graphs for LLM Grounding
This first part of the book sets the stage for building grounded, context-aware AI applications. We will start by introducing the fundamentals of Large Language Models (LLMs), the challenges they face around factuality, and how Retrieval-Augmented Generation (RAG) helps address those limitations. Next, we break down RAG architectures with practical insights and implementation guidance. We conclude by establishing a foundational understanding of knowledge graphs—highlighting how Neo4j enables structured, semantically rich representations that enhance the grounding and reasoning capabilities of LLMs.
This part of the book includes the following chapters:
- Chapter 1, Introducing LLMs, RAGs, and Neo4j Knowledge Graphs
- Chapter 2, Demystifying RAG
- Chapter 3, Building a Foundational Understanding of Knowledge Graph for Intelligent Applications
