---
type: concept
title: "Multi-Query RAG"
description: "Issuing several reformulated queries for a single question and merging their results to get broader, more complete retrieval coverage."
tags: [rag, retrieval, query-generation, coverage]
videos: [every-rag-strategy-explained-in-13-minutes-no-fluff, all-the-best-rag-strategies-in-one-mcp-for-ai-coding-assistants, the-3-must-have-mcp-servers-for-any-ai-coding-and-how-to-use-them]
created: 2026-07-21
updated: 2026-07-21
---

# Multi-Query RAG

Multi-query RAG answers one question by firing several reworded queries at the knowledge base and merging what comes back. "we're using an LLM to generate multiple different variants and then sending them into our search in parallel. And so it gives us more comprehensive coverage" [0:06:52]. A single phrasing only reaches the chunks that happen to match it; several phrasings cast a wider net, so the model sees a more complete slice of the relevant material.

## How it works

An LLM takes the user's question and produces multiple alternative formulations, each emphasizing different terms or angles. Those variants run against the retriever in parallel, and their result sets are combined and de-duplicated before reaching the generation step. The payoff is coverage: when Cole watches an agent do this in practice, "it's searching multiple different times with different queries. this is exactly what I want it to do to make sure it has a very overarching view of everything that it has to know to implement this agent." It is one of the strategies Cole groups with query expansion as a next accuracy layer to add to a RAG stack: "Things like multi-query rag and query expansion."

## Builds on

- [Retrieval-Augmented Generation (RAG)](./rag.md) - multi-query RAG is a coverage-boosting layer on the standard retrieve-then-generate pipeline.

## Contrasts with

- [Query Expansion](./query-expansion.md) - a close sibling that enriches a single query with related terms rather than generating several distinct queries; the two are often mentioned together and easily confused.

## Related

- [Agentic RAG](./agentic-rag.md) - an agent naturally does multi-query retrieval by re-searching with better phrasings until it has enough context.
- [Hybrid Search](./hybrid-search.md), [Reranking](./reranking.md) - complementary accuracy strategies stacked in the same pipeline.

## Sources

- [Every RAG Strategy Explained in 13 Minutes (No Fluff)](../sources/every-rag-strategy-explained-in-13-minutes-no-fluff.md) - "[0:06:52] we're using an LLM to generate multiple different variants and then sending them into our search in parallel. And so it gives us more comprehensive coverage"
- [The 3 MUST Have MCP Servers for Any AI Coding (and How to Use Them)](../sources/the-3-must-have-mcp-servers-for-any-ai-coding-and-how-to-use-them.md) - "[0:13:32] it's searching multiple different times with different queries. this is exactly what I want it to do to make sure it has a very overarching view of everything that it has to know to implement this agent"
- [All the BEST RAG Strategies in ONE MCP for AI Coding Assistants](../sources/all-the-best-rag-strategies-in-one-mcp-for-ai-coding-assistants.md) - "[0:18:51] I want to implement even more strategies. Things like multi-query rag and query expansion"
