multi hop
AI, ML & GenAI 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 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.🤖 Retrieval & AgentsSign in
Core
Query Transformation and Multi-Hop RetrievalA user's raw question is frequently a weak search query: ambiguous, underspecified, or needing several facts chained together. Query transformation rewrites or breaks it apart before retrieval, query rewriting, expansion, HyDE (embed a hypothetical answer), and decomposition into sub-questions. Multi-hop questions call for iterative retrieval because the second fact hinges on the first's answer. Applied-AI interviews cover it because single-shot retrieval on the raw query is a common, fixable cause of RAG failure.🤖 Retrieval & AgentsSign in