What is Graph RAG?
Graph RAG is a retrieval or retrieval-augmented generation concept. It is part of the vocabulary teams use when they compare models, design prompts or review an AI system.
Definition
Graph RAG is a retrieval or retrieval-augmented generation concept. It is part of the vocabulary teams use when they compare models, design prompts or review an AI system. The term is used in retrieval and rag discussions, where precision matters: two teams can use the same word to mean slightly different things. In this glossary, graph rag is treated as a practical concept rather than a formal specification.
Why it matters
Retrieval terms describe how external documents are indexed, searched and inserted into a prompt. They matter because most production systems rely on fresh or private data that is not in the model's weights.
Example
Suppose you are comparing GLM-4.5 Base with another model for a production task. Graph RAG is one of the factors behind the numbers you see: GLM-4.5 Base lists a 32,768-token context window, costs $3.99 per million input tokens and scores 70.6 on GPQA Diamond. Understanding the term helps you decide which of those differences actually matter for your workload.
How to evaluate it
- Check whether graph rag is defined the same way in the documentation you are reading.
- Ask which measurement or trade-off the term is describing before comparing numbers.
- Separate vendor claims from independently observed behaviour on your own data.
- Write down the definition your team uses so reviews and evaluations stay consistent.
Related terms
Related guides
FAQ
What does Graph RAG mean?
Graph RAG is a retrieval or retrieval-augmented generation concept. It is part of the vocabulary teams use when they compare models, design prompts or review an AI system.
Why does Graph RAG matter?
Retrieval terms describe how external documents are indexed, searched and inserted into a prompt. They matter because most production systems rely on fresh or private data that is not in the model's weights.
How is Graph RAG used in practice?
Suppose you are comparing GLM-4.5 Base with another model for a production task. Graph RAG is one of the factors behind the numbers you see: GLM-4.5 Base lists a 32,768-token context window, costs $3.99 per million input tokens and scores 70.6 on GPQA Diamond. Understanding the term helps you decide which of those differences actually matter for your workload.