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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RELATED CONCEPTS

The RAG Pipeline Vector Search and ANN Indexes Agentic and Corrective RAG Hybrid Search and Reciprocal Rank Fusion

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.→

COMPANIES THAT ASSUME THIS

OpenAI Anthropic Google Microsoft Scale AI Cognition

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