What is Graph RAG?
Graph RAG structures data as a knowledge graph, allowing AI to traverse relationships and context rather than just matching isolated keywords.
Traditionally, Retrieval-Augmented Generation (RAG) involves dropping raw documents into a vector database, converting text into embeddings, and letting a Large Language Model (LLM) search that space for matching ideas.
However, business data, such as internal hierarchies, software architecture, or client histories, relies heavily on the relationships between different concepts. A flat vector database often fails to capture how different pieces of data connect.
Graph RAG solves this problem by structuring unstructured text into a “knowledge graph,” a web of interconnected entities.
- Understanding Relationships: Instead of just extracting paragraphs about ‘Feature X’ and ‘Team Y’, Graph RAG organizes the data relationally (e.g., “Feature X is built by Team Y, which is managed by Person Z”).
- Complex Synthesis: This allows AI systems to mathematically trace paths through data to answer complex questions that span across multiple documents and contexts.
Together with Context Engineering, Graph RAG provides the structural backbone necessary to feed accurate, deep context into dynamic AI decision engines.
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