Traditional RAG has a fundamental limitation: vector search retrieves text, not structured data. When you need precise answers, LLMs guess aggregations from text chunks instead of executing calculations. This causes wrong averages, fabricated counts, and approximate results. Graph-RAG solves this by storing data as entities and relationships, enabling structured queries that execute calculations instead of estimating from text. Join me as I build a travel agent demo that compares both on 515K hotel reviews proving where RAG fails and how graphs deliver accurate answers.
Elizabeth Fuentes Leone is an AWS Developer Advocate at Amazon Web Services with a background in Data Science and a focus on Generative AI. She works with developers and technical communities to help them understand and build with cloud and AI technologies, sharing practical guidance on modern application development and machine learning. Originally from Venezuela and based in the United States, she brings a strong combination of technical expertise and community engagement to help teams adopt and apply AI-driven solutions.
