Graph Power Hour Ep1
GraphRAG:
The Good, the Bad, and the Ugly
Webinar Series with Paco Nathan & Featuring Dr. Clair Sullivan
Watch the second podcast of Paco Nathan’s Graph Power Hour. This week’s topic – GraphRAG: The Good, the Bad & the Ugly.
ABOUT THIS EPISODE
GraphRAG is having a moment! Thanks to the GenAI revolution, more people than ever are turning to graphs to solve problems like retrieval-augmented generation (RAG) and minimize hallucinations common in GenAI models. GraphRAG structures excel at representing complex, interconnected data—critical for improving the contextual understanding that generative AI requires. By leveraging graphs, AI systems can better discover relationships, link entities, and enrich data, making them more effective at handling real-world complexity. As AI evolves, graphs are emerging as key enablers for more sophisticated, context-aware insights.
However, applying graphs to back GenAI applications is not without challenges. Extracting nodes and relationships from unstructured text via natural language processing (NLP) is difficult, as language is rich in context, idiomatic expressions, technical jargon, and implicit meanings that don’t neatly fit into graph structures. While large language models (LLMs) reflect general language usage, they often fail to capture subtle nuances and technical details, leading to oversimplified or inaccurate graph representations. This disconnect between the richness of language and the structured nature of graphs can hinder a system’s ability to generate significant insights.
In this talk, we will explore the use of graphs “from the trenches,” examining real-world challenges encountered when applying them in complex systems. While graphs offer immense theoretical potential, translating that into practical applications often reveals unforeseen difficulties. From handling incomplete, ambiguous, or unresolved data to scaling large, interconnected datasets, the journey is rarely straightforward. We’ll also discuss the challenges of integrating graphs with AI models, where the structured nature of graphs can clash with the messiness of real-world information. These practical insights will highlight the gap between the idealized use of graphs and the realities faced in the field.
Resources
Get the slides here
Visit Dr. Clair Sullivan’s Github here
Read the Neo4 blog featuring: SafeGraph + WHISARD + PPP here
Read the guest blog for Senzing here
Dr. Clair Sullivan’s tutorial here
Retrieval-Augmented Generation for AI-Generated Content: A Survey
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Paco Nathan
Principal DevRel Engineer
Paco Nathan leads DevRel for the Entity Resolved Knowledge Graph practice area at Senzing and is a computer scientist with +40 years of tech industry experience and core expertise in data science, natural language, graph technologies, and cloud computing. He’s the author of numerous books, videos, and tutorials about these topics.