multi hop
Applied AI interview questions tagged multi hop, across every topic.
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Concepts behind "multi hop"
The curriculum that explains the ideas these questions test.
Core
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.🤖 Retrieval & AgentsSign in
Core
Query Transformation and Multi-Hop RetrievalThe user's raw question is often a poor search query: ambiguous, underspecified, or requiring several facts chained together. Query transformation rewrites or decomposes it before retrieval, query rewriting, expansion, HyDE (embed a hypothetical answer), and decomposition into sub-questions. Multi-hop questions need iterative retrieval because the second fact depends on the first's answer. Applied-AI interviews probe it because single-shot retrieval on the raw query is a common, fixable cause of RAG failure.🤖 Retrieval & AgentsSign in