NCA-GENL Question 20
Select 3A company wants to implement a retrieval-augmented generation (RAG) system to help its customer support team answer complex product-related questions. Which combination of components is MOST essential for building such a system?
- A
A large language model (LLM) fine-tuned on customer support data
- B
A vector database storing domain-specific documents
- C
A rule-based chatbot for pre-defined responses
- D
An embedding model to convert documents and queries into vector representations
- E
A summarization model to reduce the size of retrieved documents
Show answer and explanation
Correct answers: A, B, D
Explanation
A retrieval-augmented generation (RAG) system combines the capabilities of a large language model (LLM) with a document retrieval mechanism to generate high-quality, context-aware responses. The core components of RAG include an LLM for response generation, a vector database for storing and retrieving domain-specific documents, and an embedding model for creating vector representations of queries and documents. While additional models like summarizers may enhance the system, they are not fundamental to its operation.
- A. Correct.
A large language model (LLM) fine-tuned on customer support data is essential because it can generate contextual responses based on the retrieved information.
- B. Correct.
A vector database storing domain-specific documents is critical in RAG systems as it enables efficient retrieval of relevant documents based on semantic similarity.
- C. Incorrect.
A rule-based chatbot is not a core component of a RAG system, as RAG focuses on dynamic, context-aware generation rather than pre-defined responses.
- D. Correct.
An embedding model to convert documents and queries into vector representations is a key component of RAG, as it allows for effective similarity searches in the vector database.
- E. Incorrect.
A summarization model is optional in a RAG system. While it can help condense retrieved documents, it is not an essential component for building the system.