AI / LlamaIndex Interview Questions

How does the SubQuestionQueryEngine work?

SubQuestionQueryEngine is designed for complex questions that really require pulling information from multiple sources or multiple angles of the same source, something a single retrieval pass often can't answer well.

Given a query, it first uses an LLM to break the question into smaller sub-questions, each targeted at one of the underlying query engine tools it was configured with. For example, "Compare Q1 and Q2 revenue" might be split into "What was Q1 revenue?" and "What was Q2 revenue?", each routed to the relevant document's query engine.

Each sub-question is answered independently, and the final step synthesizes those sub-answers into one coherent response to the original question. This is especially useful for comparison or multi-document questions where a single embedding search over everything at once would blur the distinct pieces of information together.

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SubQuestionQueryEngine is most useful for: Single-fact lookups in one document

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Complex questions needing information pulled from multiple sources or angles

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Loading files from disk

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Storing embeddings in a vector database

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The first step SubQuestionQueryEngine performs is: Directly answering the query with no decomposition

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Using an LLM to break the query into sub-questions routed to query engine tools

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Deleting irrelevant documents

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Training a new embedding model

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More Related questions...

What is LlamaIndex? What is the purpose of LlamaIndex in a RAG pipeline? What are Documents and Nodes in LlamaIndex? What is a VectorStoreIndex? What are the types of indices in LlamaIndex? What is a query engine in LlamaIndex? What is a retriever in LlamaIndex? What is a response synthesizer? What is Settings in LlamaIndex? What is a node parser or text splitter in LlamaIndex? How do you use SimpleDirectoryReader? What is LlamaHub? Describe the ingestion pipeline in LlamaIndex? What is a chat engine in LlamaIndex? What are the response modes available in LlamaIndex query engines? What is the difference between VectorStoreIndex and SummaryIndex? How does similarity_top_k affect retrieval? What is the difference between a query engine and a chat engine? How do node postprocessors work in LlamaIndex? Why should you use metadata filtering in retrieval? What is the difference between refine and compact response modes? How does the SubQuestionQueryEngine work? What is a RouterQueryEngine and when would you use it? Why is chunk size important in LlamaIndex? How do you persist and reload an index in LlamaIndex? What is the difference between LlamaIndex and LangChain? How does the SentenceWindowNodeParser improve retrieval quality? When should you use auto-merging retrieval? What is HyDE and how does it help retrieval? How do you integrate a custom vector store like Pinecone or Chroma with LlamaIndex? What is the difference between ReActAgent and FunctionCallingAgent? How does LlamaIndex support structured data querying such as SQL? Why use CohereRerank or LLMRerank as a node postprocessor? What is the role of the CallbackManager in LlamaIndex? How do you evaluate a LlamaIndex RAG pipeline for faithfulness? Explain the execution flow of a query in a VectorStoreIndex-based query engine? Explain the internal working of the IngestionPipeline caching mechanism? Explain the lifecycle of a Node from Document to retrieval? What is the difference between PropertyGraphIndex and KnowledgeGraphIndex? How can you optimize token usage and cost in a large-scale LlamaIndex deployment? Explain the internal working of AgentWorkflow and event-driven workflows in LlamaIndex? How do you troubleshoot poor retrieval relevance in a LlamaIndex application? What happens internally when you call index.as_query_engine()? How does LlamaIndex handle asynchronous querying at scale? Explain the difference between the low-level composition API and the high-level API in LlamaIndex? Why doesn't increasing similarity_top_k always improve answer quality? How do you design a hybrid search system combining vector and keyword retrieval in LlamaIndex? Explain the internal working of tree_summarize response synthesis? How would you architect a multi-tenant LlamaIndex application with metadata filtering per tenant? Which is better and why: sentence-window retrieval vs auto-merging retrieval for long documents?

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AI / LlamaIndex Interview Questions

How does the SubQuestionQueryEngine work?

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