NCA-GENL Question 23
Select 3You are tasked with designing a Retrieval-Augmented Generation (RAG) pipeline for a customer support chatbot that must answer domain-specific questions. Which of the following steps is essential for building this solution?
- A
Indexing a domain-specific knowledge base or document repository
- B
Training a new large language model from scratch on domain-specific data
- C
Integrating a retrieval mechanism to fetch relevant documents based on user queries
- D
Using a pre-trained LLM to generate responses based on retrieved documents
- E
Restricting the chatbot to generate responses only from the knowledge base without any retrieval
Show answer and explanation
Correct answers: A, C, D
Explanation
Retrieval-Augmented Generation (RAG) combines retrieval mechanisms with pre-trained LLMs to improve the relevance and accuracy of generated responses. The knowledge base must be indexed (Step 1) to allow retrieval (Step 3), and a pre-trained LLM is used to generate responses based on the retrieved information (Step 4). Training a new LLM from scratch is unnecessary, and restricting the chatbot to static responses contradicts the dynamic nature of RAG.
- A. Correct.
Indexing a domain-specific knowledge base is a critical step in RAG as it enables the retrieval mechanism to locate relevant information for user queries.
- B. Incorrect.
Training a new large language model from scratch is not necessary in most RAG use cases since pre-trained LLMs can be fine-tuned or used directly in combination with retrieval mechanisms.
- C. Correct.
Integrating a retrieval mechanism is an essential part of RAG, as it allows fetching relevant domain-specific documents to improve response accuracy.
- D. Correct.
Using a pre-trained LLM to process the retrieved documents and generate responses is a key component of RAG, ensuring that the model leverages both the knowledge base and its existing language understanding.
- E. Incorrect.
Restricting the chatbot to only generate responses from the knowledge base without retrieval would limit its ability to dynamically incorporate user-specific context, which is against the principles of RAG.