The introduction to Knowledge Graphs for Production AI: Build, Deploy, and Scale LLM-Powered Systems with Neo4j, RAG, and Semantic Search aims to set the stage for why knowledge graphs represent a critical advancement in making large language model (LLM) applications reliable, scalable, and enterprise-ready. This book targets AI engineers, data engineers, machine learning practitioners transitioning to LLM-based systems, startup founders building AI products, and consultants advising organizations on production AI deployments. The content assumes intermediate technical familiarityâreaders should understand basic Python programming, vector embeddings, and LLM promptingâbut avoids heavy mathematical derivations or purely academic graph theory. Instead, the focus remains on practical, production-oriented guidance that bridges the gap between prototype experiments and revenue-generating systems.
The introduction to Knowledge Graphs for Production AI: Build, Deploy, and Scale LLM-Powered Systems with Neo4j, RAG, and Semantic Search aims to set the stage for why knowledge graphs represent a critical advancement in making large language model (LLM) applications reliable, scalable, and enterprise-ready. This book targets AI engineers, data engineers, machine learning practitioners transitioning to LLM-based systems, startup founders building AI products, and consultants advising organizations on production AI deployments. The content assumes intermediate technical familiarityâreaders should understand basic Python programming, vector embeddings, and LLM promptingâbut avoids heavy mathematical derivations or purely academic graph theory. Instead, the focus remains on practical, production-oriented guidance that bridges the gap between prototype experiments and revenue-generating systems.