Your LLM keeps hallucinating, and clients are beginning to lose trust. Generative AI can amaze users one moment and confuse them the next when answers are based on guesswork rather than verified facts. What if you could design systems that deliver accurate, traceable, and relevant information every time? By combining knowledge graphs with retrieval-augmented generation, you can build solutions that power GenAI models with structured, reliable data and keep stakeholders confident in every interaction.
- Knowledge graph basics: Model context data for instant, precise retrieval.
- Vector similarity search toolkit: Surface only the most relevant passages, cut noise.
- Agentic RAG workflow: Orchestrate multi-step reasoning that scales to production.
- Cypher and Python templates: Drop-in code accelerates prototypes to deployable services.
- Evaluation framework: Measure accuracy, latency, and traceability with confidence.
- Hybrid structured plus unstructured guidance: Integrate PDFs, databases, and APIs into one coherent knowledge base.
Essential GraphRAG by graph experts Tomaž Bratanič and Oskar Hane arrives to show data teams exactly how to hard-wire reliability into GenAI projects.
Through concise explanations and fully worked examples, the authors guide you from raw text to a Neo4j-backed knowledge graph powering Retrieval Augmented Generation. Each chapter pairs theory with runnable notebooks, so you see instant results.
Finish the book able to architect, build, and benchmark a production-ready RAG pipeline that your stakeholders can audit and trust. The techniques transfer to any domain and future model.
For data scientists and Python developers with basic Neo4j skills who want bulletproof GenAI, this is your next step.
