AppliedAIPrep logoAppliedAI/Prep

graph rag

Applied AI interview questions tagged graph rag, across every topic.

2 questions · 0 unlocked for you
Open full answer →
Open full answer →

Concepts behind "graph rag"

The curriculum that explains the ideas these questions test.

Core
🤖 Retrieval & AgentsSign in
GraphRAG and Knowledge-Graph RetrievalGraphRAG builds an entity-and-relationship graph over a corpus, then retrieves by traversing that graph instead of (or alongside) flat vector similarity. It answers the questions flat RAG fails on: multi-hop connections that span documents and global questions that need the whole corpus summarized, not the top-k chunks. The catch is build and maintenance cost: extracting entities and relations with an LLM is expensive and the graph drifts as the corpus changes. Applied-AI interviews probe it to see if you know when the extra machinery actually pays off.
Explore the full curriculum →