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graph rag

AI, ML & GenAI interview questions tagged graph rag, across every topic.

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Concepts behind "graph rag"

The curriculum that explains the ideas these questions test.

Core
🤖 Retrieval & AgentsSign in
GraphRAG and Knowledge-Graph RetrievalGraphRAG constructs an entity-and-relationship graph across a corpus, then retrieves by walking that graph rather than (or together with) flat vector similarity. It handles the questions flat RAG cannot: multi-hop links that span documents and global questions needing the whole corpus summarized instead of the top-k chunks. The downside is build and upkeep cost: pulling entities and relations with an LLM runs expensive and the graph drifts as the corpus shifts. Applied-AI interviews cover it to check whether you know when the added machinery earns its keep.
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