"Production GraphRAG: Building Knowledge Graph Retrieval for Verifiable Enterprise Answers" Enterprise AI systems are no longer judged only by fluency—they are judged by whether their answers can be trusted, traced, and defended. This book is written for experienced engineers, architects, data platform leaders, and applied AI practitioners who need to move beyond generic RAG and build GraphRAG systems that stand up to enterprise scrutiny. It addresses the real production challenge: delivering answers that are not just useful, but verifiable under operational, regulatory, and business constraints. Across the book, readers will learn how to design retrieval-ready knowledge graphs, build ingestion pipelines with durable evidence linkage, plan hybrid retrieval across graph, lexical, and vector operators, and assemble grounded context for constrained answer generation. It also covers citation and provenance design, abstention and contradiction handling, evaluation frameworks for support and faithfulness, and the operational disciplines required for observability, security, deployment, rollback, and scale. The emphasis throughout is on architectural decisions, trade-offs, and production-grade implementation patterns. Rather than treating GraphRAG as a collection of isolated techniques, the book presents it as a coherent enterprise system. Readers should already be comfortable with modern retrieval and LLM application design; in return, they will gain a rigorous blueprint for building systems that are explainable, auditable, and maintainable in high-stakes environments.
