← 🤖 Retrieval & AgentsNEXT IN RETRIEVAL & AGENTSHierarchical Retrieval (RAPTOR and Small-to-Big)→
Core
GraphRAG and Knowledge-Graph Retrieval
GraphRAG 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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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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