Your RAG agent seems smart, until you ask it to count something. "How many items match criteria X?" Traditional RAG fabricates: "approximately 45-50." The real answer from your data? 133. Vector similarity can't count, aggregate, or reason across relationships. The fundamental limitation: Traditional RAG retrieves text chunks by similarity, then asks the LLM to synthesize answers. This works for simple lookups but fails systematically on four query types: counting ("how many?"), aggregation ("what's the average?"), multi-hop reasoning ("what's available at the highest-rated?"), and out-of-domain detection ("any results in Antarctica?". RAG fabricates, Graph-RAG correctly says "none"). I will cover why traditional RAG hallucinates on structured queries (the architectural root cause), how Graph-RAG builds knowledge graphs automatically using neo4j-graphrag without manual schema design, the Text2Cypher pattern that converts natural language into precise database queries the LLM cannot fabricate, a side-by-side comparison on identical queries showing RAG fabrication vs Graph-RAG precision, and production implementation patterns with open-source tools. You'll walk away with: • Graph-RAG implementation with Neo4j and auto entity extraction for any document set • Text2Cypher query generation to get precise answers from knowledge graphs • A concrete decision framework for when to use RAG vs Graph-RAG • Hybrid architecture patterns: Graph-RAG for structured queries, RAG for unstructured • Open-source code adaptable to any domain with structured data (product catalogs, FAQs, inventories) Most RAG talks focus on embeddings and retrieval tuning. This addresses RAG's fundamental limitation: statistical hallucinations on structured data. The solution (knowledge graphs + Cypher) is domain-agnostic and applies wherever your documents contain countable, aggregatable, or relationship-rich data.

Outline: • The RAG Hallucination Problem • Graph-RAG Architecture • Live Implementation • Production Patterns • Decision Framework