Retrieval-Augmented Generation (RAG) Objective Questions and Answers

This RAG quiz contains carefully curated objective questions with correct answers and clear explanations. It is designed for developers and AI engineers preparing for real-world LLM applications, covering retrieval pipelines, vector search, embeddings, ranking, and optimization techniques.

Practice Retrieval-Augmented Generation (RAG) MCQs with Detailed Explanations

Answer at least 12 questions to submit and verify answers.

1 What does RAG stand for? Easy

2 What is the main goal of RAG? Easy

3 Which component retrieves relevant documents? Medium

4 Which component generates the final answer? Medium

5 What is an embedding? Medium

6 Which database is commonly used for RAG? Medium

7 What is cosine similarity used for? Medium

8 What is chunking in RAG? Medium

9 Which improves retrieval quality? Medium

10 What is hybrid search? High

11 What is BM25? High

12 What is re-ranking in RAG? High

13 Which improves grounding? Medium

14 What is vector indexing? Medium

15 Which metric is used in ANN search? High