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AI MERSION 25'-RAG

Total questions: 10

Worksheet time: 5mins

Name
Class
Date
1.

What does RAG stands for ?

a)

Randomized AI Generation

b)

Retrieval-Augmented Generation

c)

Rapid Algorithmic Growth

d)

Recursive AI Graph

2.

What are the two main components of a RAG?

a)

Encoder and Decoder

b)

Retriever and Generator

c)

Searcher and Filter

d)

Input and Output

3.

What is the main goal of using RAG technology?

a)

To make AI faster

b)

To generate images

c)

To improve factual accuracy using external data

d)

To store data

4.

How is RAG different from a normal language model?

a)

RAG uses external data sources

b)

RAG only works offline

c)

RAG cannot generate text

d)

RAG is only for coding

5.

What is meant by 'augmentation' in RAG?

a)

Removing data

b)

Adding external information to the model

c)

Compressing model size

d)

Training a new model

6.

Which component of RAG finds relevant documents?

a)

Retriever

b)

Generator

c)

Processor

d)

Vectorizer

7.

What type of database is commonly used in RAG for storing indexed documents?

a)

Vector database

b)

SQL database

c)

File system

d)

Distributed ledger

8.

Which frameworks are commonly used in RAG for combining retrieval and generation?

a)

LangChain

b)

LlamaIndex

c)

Hadoop

d)

Both A and B

9.

What problem does RAG primarily address in traditional LLMs?

a)

Slow processing speeds

b)

Outdated knowledge

c)

Lack of creativity

d)

High memory use

10.

Which technique is used to convert text into numerical format in RAG?

a)

One-hot encoding

b)

Vector embeddings

c)

Binary encoding

d)

Random projections