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Core
Query Transformation and Multi-Hop Retrieval
The 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.
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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 query transformation in RAG (HyDE, decomposition, step-back), and when does each help?→RAG & Agent System DesignWhat is HyDE (Hypothetical Document Embeddings), and why does it improve retrieval?→RAG & Agent System DesignHow do you decompose a complex query into sub-queries for retrieval, and when does it backfire?→RAG & Agent System DesignWhen does HyDE hurt retrieval, and what variants fix its failure modes?→RAG & Agent System DesignWhat is GraphRAG, and when does it beat traditional vector RAG?→RAG & Agent System DesignDesign a production RAG system over 10M documents serving ~1,000 QPS at sub-second latency.→
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