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.

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.