Retrieval-Augmented Generation (RAG) Objective Questions and Answers

Test your skills with RAG (Retrieval-Augmented Generation) objective questions covering embeddings, vector databases, semantic search, chunking strategies, hybrid retrieval, and LLM integration techniques.

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.

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