← 🤖 Retrieval & AgentsNEXT IN RETRIEVAL & AGENTSHierarchical Retrieval (RAPTOR and Small-to-Big)→
Core
GraphRAG and Knowledge-Graph Retrieval
GraphRAG 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.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
RAG & Agent System DesignWhat is GraphRAG, and when does it beat traditional vector RAG?→RAG & Agent System DesignHow do you decompose a complex query into sub-queries for retrieval, and when does it backfire?→RAG & Agent System DesignDesign a production RAG system over 10M documents serving ~1,000 QPS at sub-second latency.→RAG & Agent System DesignContext windows are now huge. When do you just stuff everything in context instead of building RAG?→RAG & Agent System DesignWhat is query transformation in RAG (HyDE, decomposition, step-back), and when does each help?→RAG & Agent System DesignWhat is Self-RAG / adaptive retrieval, and how does the model decide when to retrieve?→
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