What is the purpose of splitting documents into smaller overlapping chunks in a RAG system?

ATo simplify the process of training the embedding model

BTo enable the matching of different relevant passages to user queries

CTo improve the efficiency of encoding queries into vector representations

DTo reduce the storage space required for the vector database

Answer : B

What is a part of LLM context optimization?

AReducing the model's size to improve efficiency

BAdjusting the model's output format and style

CEnhancing the computational speed of the model

DProviding the model with domain-specific knowledge needed to solve a problem

Answer : D

What is the goal of prompt engineering?

ATo replace human decision-making with automated processes

BTo craft inputs that guide Al systems in generating desired outputs

CTo optimize hardware performance for Al computations

DTo develop new neural network architectures for Al models

Answer : B

Which of the following statements accurately describe the RAG process? Note: There are 2 correct ans-wers to this question.

AThe user's questi on is used to search a knowledge base or a set of documents.

BThe embedding model stores the generated ans wers for future reference.

CThe retrieved content is combined with the LLM's capabilities to generate a response.

DThe LLM directly ans wers the user's question without accessing external information.

Answer : A, C

What is the primary function of the embedding model in a RAG system?

ATo generate responses based on retrieved documents and user queries

BTo encode queries and documents into vector representations for comparison

CTo evaluate the faithfulness and relevance of generated Answers

DTo store vector representations of documents and search for relevant passages

Answer : B

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