question: What is RAG?

gold_answer: TODO: fill with verified answer from your document

expected_terms: retrieval augmented generation

difficulty: easy


question: What does RAG stand for?

gold_answer: TODO: fill with verified answer from your document

expected_terms: retrieval-augmented generation

difficulty: easy


question: What is a vector database?

gold_answer: TODO: fill with verified answer from your document

expected_terms: vector database embeddings

difficulty: easy


question: What is chunking in document processing?

gold_answer: TODO: fill with verified answer from your document

expected_terms: chunk text split

difficulty: easy


question: What is an embedding model?

gold_answer: TODO: fill with verified answer from your document

expected_terms: embedding model vector

difficulty: easy


question: What is a knowledge graph?

gold_answer: TODO: fill with verified answer from your document

expected_terms: knowledge graph entities relations

difficulty: easy


question: What is semantic search?

gold_answer: TODO: fill with verified answer from your document

expected_terms: semantic search meaning

difficulty: easy


question: What is BM25?

gold_answer: TODO: fill with verified answer from your document

expected_terms: bm25 keyword retrieval

difficulty: easy


question: What is a cross-encoder?

gold_answer: TODO: fill with verified answer from your document

expected_terms: cross-encoder reranking

difficulty: easy


question: What is document indexing?

gold_answer: TODO: fill with verified answer from your document

expected_terms: index document search

difficulty: easy


question: What is hallucination in LLMs?

gold_answer: TODO: fill with verified answer from your document

expected_terms: hallucination incorrect generated

difficulty: easy


question: What is a citation in a RAG system?

gold_answer: TODO: fill with verified answer from your document

expected_terms: citation source reference

difficulty: easy


question: What is context in answer generation?

gold_answer: TODO: fill with verified answer from your document

expected_terms: context retrieved generation

difficulty: easy


question: What is hybrid retrieval?

gold_answer: TODO: fill with verified answer from your document

expected_terms: hybrid keyword vector

difficulty: easy


question: What is a document graph?

gold_answer: TODO: fill with verified answer from your document

expected_terms: document graph entities

difficulty: easy


question: How does hybrid retrieval combine keyword and vector search?

gold_answer: TODO: fill with verified answer from your document

expected_terms: hybrid keyword vector retrieval

difficulty: medium


question: Explain how reranking improves retrieval quality.

gold_answer: TODO: fill with verified answer from your document

expected_terms: reranking relevance cross-encoder

difficulty: medium


question: How are entities extracted from document chunks?

gold_answer: TODO: fill with verified answer from your document

expected_terms: entity extraction chunk

difficulty: medium


question: What is the role of the knowledge graph in answering questions?

gold_answer: TODO: fill with verified answer from your document

expected_terms: graph context entities relations

difficulty: medium


question: How does the system ground its answers in source documents?

gold_answer: TODO: fill with verified answer from your document

expected_terms: citation source grounded

difficulty: medium


question: How does the entity normalization process work?

gold_answer: TODO: fill with verified answer from your document

expected_terms: normalization entity lowercase

difficulty: medium


question: How are relations detected between entities?

gold_answer: TODO: fill with verified answer from your document

expected_terms: relation co-occurrence sentence

difficulty: medium


question: What is graph-guided retrieval?

gold_answer: TODO: fill with verified answer from your document

expected_terms: graph guided retrieval chunks

difficulty: medium


question: How does the system handle noisy entities?

gold_answer: TODO: fill with verified answer from your document

expected_terms: noisy filter stop entity

difficulty: medium


question: How does the evidence extraction step work?

gold_answer: TODO: fill with verified answer from your document

expected_terms: evidence extraction sentence

difficulty: medium


question: What happens when the LLM answer is not good enough?

gold_answer: TODO: fill with verified answer from your document

expected_terms: fallback quality check

difficulty: medium


question: How does the prompt builder create grounded prompts?

gold_answer: TODO: fill with verified answer from your document

expected_terms: prompt grounded evidence

difficulty: medium


question: What is the role of the source viewer?

gold_answer: TODO: fill with verified answer from your document

expected_terms: source viewer verification

difficulty: medium


question: How does document comparison work?

gold_answer: TODO: fill with verified answer from your document

expected_terms: comparison document side-by-side

difficulty: medium


question: What information does the admin dashboard show?

gold_answer: TODO: fill with verified answer from your document

expected_terms: admin dashboard monitoring

difficulty: medium


question: Compare the retrieval results with and without graph-guided retrieval.

gold_answer: TODO: fill with verified answer from your document

expected_terms: graph retrieval fusion chunks

difficulty: hard


question: What are the limitations of rule-based entity extraction?

gold_answer: TODO: fill with verified answer from your document

expected_terms: rule-based entity limitation

difficulty: hard


question: How does the graph fusion strategy decide which chunks to boost or add?

gold_answer: TODO: fill with verified answer from your document

expected_terms: fusion graph score boost

difficulty: hard


question: If two documents discuss the same topic, how does comparison mode surface differences?

gold_answer: TODO: fill with verified answer from your document

expected_terms: comparison document differences

difficulty: hard


question: How does the system filter noisy entities from the graph?

gold_answer: TODO: fill with verified answer from your document

expected_terms: noisy filter quality entity

difficulty: hard


question: What is the effect of graph_entity_limit on answer quality?

gold_answer: TODO: fill with verified answer from your document

expected_terms: graph_entity_limit quality entities

difficulty: hard


question: How does the graph context text get injected into the prompt?

gold_answer: TODO: fill with verified answer from your document

expected_terms: graph context prompt evidence

difficulty: hard


question: What determines the weight of a relation in the graph?

gold_answer: TODO: fill with verified answer from your document

expected_terms: weight relation co-occurrence

difficulty: hard


question: How are graph-supported chunks scored differently from normal chunks?

gold_answer: TODO: fill with verified answer from your document

expected_terms: graph score boost supported

difficulty: hard


question: What trade-offs exist between graph retrieval top-k and answer latency?

gold_answer: TODO: fill with verified answer from your document

expected_terms: top_k latency trade-off

difficulty: hard


question: How does the verb relation map affect edge labeling?

gold_answer: TODO: fill with verified answer from your document

expected_terms: verb relation edge label

difficulty: hard


question: How does the system prevent relation explosion in dense chunks?

gold_answer: TODO: fill with verified answer from your document

expected_terms: relation explosion limit combinations

difficulty: hard


question: What is the impact of chunk size on entity extraction quality?

gold_answer: TODO: fill with verified answer from your document

expected_terms: chunk size entity extraction

difficulty: hard


question: How would switching from rule-based to NER-based extraction change the graph?

gold_answer: TODO: fill with verified answer from your document

expected_terms: ner rule-based extraction graph

difficulty: hard


question: Under what conditions does graph-guided retrieval not improve results?

gold_answer: TODO: fill with verified answer from your document

expected_terms:
graph
retrieval
no improvement

difficulty: hard


question: How does the answer quality checker decide to use the fallback?

gold_answer: TODO: fill with verified answer from your document

expected_terms: quality checker fallback threshold

difficulty: hard


question: What is the role of mention_count in entity ranking?

gold_answer: TODO: fill with verified answer from your document

expected_terms: mention_count entity ranking

difficulty: hard


question: How are chunk_ids linked between graph entities and retrieval results?

gold_answer: TODO: fill with verified answer from your document

expected_terms: chunk_id graph retrieval link

difficulty: hard


question: What happens when graph context returns zero matched entities?

gold_answer: TODO: fill with verified answer from your document

expected_terms: zero entities fallback graph

difficulty: hard


question: How could the evaluation framework be extended with human judges?

gold_answer: TODO: fill with verified answer from your document

expected_terms: evaluation human judges annotation

difficulty: hard