🤖 AI EngineeringID:
query-rewriting-decompositionQuery Rewriting & Decomposition
查询重写与多跳子查询解构
🎯Core Definition
Query Rewriting & Sub-Query Decomposition is a core Pre-Retrieval RAG optimization technique that transforms raw user prompts into structured retrieval intents; using specialized prompts or fine-tuned LLMs, it performs coreference resolution (e.g., resolving 'its latest report' into 'Apple Inc. 2026 Q2 Financial Report'), synonym query expansion, and breaks complex multi-hop queries down into parallel or sequential atomic sub-queries that query the index independently before consolidating.
💡Use Cases
Multi-turn conversational RAG, multi-entity comparative analysis (e.g., 'Compare Nvidia and Qualcomm newest chip efficiency and price'), and ambiguous long-tail questions.
⚡Key Problems Solved
Raw user questions are colloquially ambiguous, pronoun-laden, and multi-faceted; searching composite queries directly yields poor vector similarity and misses cross-document facts; decomposing into atomic queries and aggregating results unlocks multi-hop reasoning.
🎯5 High-Frequency Exam Points
1
Describe the prompt pattern for performing conversational coreference resolution and synthesizing standalone queries from chat history?
2
Analyze latency vs accuracy trade-offs between parallel sub-query dispatch and sequential DAG step-by-step query execution?
3
How does Multi-Query Retrieval generate 3-5 diverse synonym query variations to maximize overall retrieval recall?
4
How does Step-Back Prompting generate high-level abstracted concept queries to augment RAG background retrieval?
5
How to constrain query rewriting LLM prompts to prevent hallucinated over-generalization drifting from the user's core intent?
📖 In-depth Guide:📄 naive-and-advanced-rag →
Updated 2026-08-14🎯
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