Graph RAG
Words made simple
A few useful meanings before you read the explanation.
- RAG
- Find relevant information first, then give it to a model to help answer the question. The answer is only as reliable as the information and checks used.
- Retrieval
- Finding and fetching information that is useful for the current question.
Overview
- Graph RAG retrieves through entities and relationships, such as customer → policy → endorsement → covered asset.
- It can combine graph traversal, document retrieval, and generated summaries.
- A graph represents connected facts; it is separate from LangGraph's execution graph.
Why this matters
Some questions depend on relationships across many records rather than local text similarity.
An everyday example
A family tree helps answer relationship questions that a pile of biographies makes difficult.
A technical example
Traverse Policy P1 → modifiedBy E7, then retrieve E7's original text for the answer.
How it works
- Define entities/relations
- Ingest verified or reviewed links
- Query/traverse relevant neighborhoods
- Retrieve source evidence
- Explain with provenance.
Architecture and flow
Verified records/documents → entity graph → traversal + text evidence → answer.
The insurance assistant example
Find products affected by a policy amendment and connect each result to the source amendment.
When to use it
Relationship-heavy knowledge, cross-document analysis, and verified entity reasoning.
When to avoid it
A small FAQ collection rarely justifies entity extraction, graph maintenance, and another database.
Interview answer
“Graph RAG uses relationships to retrieve connected evidence. It is useful when entity links matter and ordinary text retrieval cannot answer reliably.”
Benefits, trade-offs, and production considerations
- Model-extracted relationships can be wrong.
- Store evidence per edge, validity dates, and ACLs; do not convert a guessed relationship into an authoritative business fact.
- SQL joins may solve structured relationships more simply.
Can you explain it without the notes?
Graph RAG
Use your own words. Explain an example and one limitation before checking the explanation.
Check your explanation
Graph RAG retrieves through entities and relationships, such as customer → policy → endorsement → covered asset. It can combine graph traversal, document retrieval, and generated summaries. A graph represents connected facts; it is separate from LangGraph's…
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Next lesson: RAG evaluation and comparison with alternativesAdapted from 13_AI.MD · “Graph RAG”. Examples are study fragments; framework behavior can depend on your version.