Multi-hop queries
Questions needing multiple steps or data sources to find the full answer.
Imagine asking your AI assistant a question that requires it to look up several pieces of information and then put them together. This is a multi-hop query.
For example, if you ask, "What is the current weather in the capital of France, and what is its population?" The AI first needs to know that Paris is the capital of France. Then it needs to find the current weather in Paris. Finally, it needs to find the population of Paris. Each of these steps is a 'hop'.
Multi-hop queries involve a sequence of information retrieval or reasoning steps to arrive at a final answer. Each step, or 'hop', typically relies on the output of the previous step. This contrasts with single-hop queries that can be answered by retrieving a single fact or document.
These queries are challenging because they require robust planning, execution, and error handling across multiple stages. Systems must maintain context, manage intermediate results, and potentially reformulate sub-queries based on retrieved information.
Multi-hop queries are gaining prominence as AI systems move beyond simple fact retrieval to more complex reasoning. Advances in agentic frameworks and retrieval augmented generation (RAG) allow models to break down complex questions and iteratively gather information, as seen in Google's recent work.
- Unlocking dependable responses with Gemini Enterprise Agent Platform’s Agentic RAGGoogle · Google Research Blog ·
- Google Gemini
- Google's Agentic RAG framework is designed to handle multi-hop queries by iteratively searching for and synthesizing information from multiple sources.
- Complex queries
- Complex queries might be difficult but can often be answered wi
- Multi-turn conversations
- Multi-turn conversations involve a back-and-forth dial
- Fact-checking
- Fact-checking verifies the accuracy of a statement, which might i
The concept of multi-hop reasoning has roots in knowledge graph research and question answering systems that needed to infer answers by traversing relationships between entities. Source ↗
Ask an AI assistant a question that requires combining information from two distinct domains. For example, 'What is the average lifespan of a golden retriever, and what is the current price of gold?' See if it can provide both pieces of information accurately.
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Questions people ask
- Why are multi-hop queries hard for AI?
- They are hard because the AI needs to plan multiple steps, manage information between steps, and handle potential errors at each stage without losing track of the original goal.
- How do AI systems handle multi-hop queries?
- Advanced systems use techniques like breaking down the query into sub-questions, using retrieval augmented generation to find information, and employing reasoning chains to connect the pieces.
First seen on Subvolts Jun 5, 2026. Updated Sep 15, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.