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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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Agent Engineering·Agents in the interviewPremium12mThe sentences that mark someone who has run oneThe final lesson. Six specific things a candidate says that could only come from operating an agent rather than building one, and the three claims that reliably mark the opposite.Applied AI Engineering·The interviewPremium12mTurning this course into a study planA concrete four-week plan mapping the seven modules onto the question bank, plus what to do differently if your interview is next week rather than next month.
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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