From Vector Search to GraphRAG: Architecting AI Systems That Actually Work in Production

Session track: AI Engineering

Session time: 11:30 am - 12:00 pm UTC November 12

Session description:

Many AI projects start with impressive demos but struggle when deployed in production. Hallucinations, missing context, poor retrieval quality, and disconnected data often prevent organizations from realizing the full value of Generative AI. In this session, Emmanuela will demonstrate how graph technologies can help bridge the gap between AI prototypes and production-ready systems. Attendees will explore the limitations of traditional Retrieval-Augmented Generation (RAG) architectures that rely solely on vector search and learn how GraphRAG approaches use knowledge graphs to provide richer context, improve explainability, and increase retrieval accuracy. The session will walk through the architecture of a modern AI system, including data ingestion, knowledge graph construction, vector embeddings, retrieval orchestration, large language model integration, and observability. Using real-world architecture patterns, attendees will learn how graphs help connect entities, relationships, and business context that are often lost in traditional retrieval systems. Attendees will also discover practical strategies for reducing hallucinations, improving agent reasoning, implementing context-aware retrieval, and designing scalable AI solutions that can evolve with changing business requirements. By the end of the session, you will understand when to use vector search, when to introduce graph-based retrieval, how GraphRAG improves enterprise AI outcomes, and the architectural patterns required to move from AI hype to reliable production systems.