When would GraphRAG beat a pure vector store?
PICTURE THIS: SQL JOIN
Simple meaning
When answers depend on entities and relations across many documents, a graph can traverse links that nearest-neighbor search never joins.
WHY — RAG instead of guessing?
Why interviewers care about RAG:
on RAG.
the situation, the default choice, and one exception - that reads as experience.
Name the idea, why it exists, then one short example.
End with when you use it and one common pitfall.
STEPS — What happens step by step?
Before you speak the answer, walk the interviewer through these steps:
- 1When answers depend on
entities and relations across many documents, a graph can traverse links that nearest-neighbor search never joins.
- 2Community summaries help global
questions over a corpus.
- 3Graphs add extraction error
and ops cost, so start with hybrid search unless evals demand it.
- 4Give an example
One tiny concrete case you can say aloud.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
When answers depend on entities and relations across many documents, a graph can traverse links that nearest-neighbor search never joins. Community summaries help global questions over a corpus.