Multi-query retrieval is the strategy of generating multiple query variants for one information need and retrieving with each to improve coverage - it increases recall by exploring different semantic angles.

What Is Multi-query retrieval?

• Definition: Retrieval approach that decomposes or reformulates a query into diverse sub-queries.
• Variant Sources: LLM paraphrases, subtopic prompts, intent facets, or domain-specific rewrites.
• Fusion Step: Results are merged, deduplicated, and reranked into a unified candidate list.
• Pipeline Role: Improves first-stage evidence discovery before generation.

Why Multi-query retrieval Matters

• Recall Expansion: Captures documents missed by single-query lexical or semantic mismatch.
• Complex Question Support: Better handles broad or multi-faceted user requests.
• Robustness Gain: Reduces dependence on one imperfect query phrasing.
• RAG Reliability: More complete evidence sets improve grounded answer quality.
• Tradeoff: Increases retrieval compute and requires stronger dedup and ranking controls.

How It Is Used in Practice

• Variant Budgeting: Limit number of generated queries by latency constraints.
• Result Fusion: Apply reciprocal rank fusion or learned merging with duplicate suppression.
• Adaptive Triggering: Use multi-query only when baseline retrieval confidence is low.

Multi-query retrieval is a practical coverage-boosting technique in RAG pipelines - diversified query generation plus robust fusion often yields meaningful improvements on difficult information needs.