NCA-GENM Question 121
Select 3You are tasked with building a retrieval-augmented generation (RAG) system to provide accurate, document-based answers in a customer support chatbot. Which of the following steps are essential to implement RAG effectively?
- A
Index the relevant documents into a vector database using embeddings.
- B
Train a generative language model from scratch on customer support data.
- C
Retrieve relevant documents based on user queries using similarity search.
- D
Fine-tune a large language model (LLM) to better handle conversational tone.
- E
Combine retrieved documents with the LLM to generate context-aware responses.
Show answer and explanation
Correct answers: A, C, E
Explanation
Retrieval-augmented generation (RAG) combines document retrieval with generative AI to produce accurate and context-aware responses. The essential steps involve indexing documents in a vector database, retrieving relevant information based on queries, and using that information to guide the generation of responses. Training a model from scratch or fine-tuning for tone, while potentially useful in certain scenarios, is not required to implement the core functionality of RAG.
- A. Correct.
Indexing documents into a vector database is a core step in RAG, as it allows the system to efficiently retrieve relevant information using embeddings.
- B. Incorrect.
Training a generative language model from scratch is not required for RAG. Instead, pre-trained LLMs are typically used, as they save significant time and resources.
- C. Correct.
Retrieving relevant documents using similarity search is critical in RAG to ensure the system provides factually accurate and contextually relevant responses.
- D. Incorrect.
Fine-tuning an LLM for conversational tone is helpful for chatbots but is not a mandatory step specific to implementing RAG.
- E. Correct.
Combining retrieved documents with the LLM ensures that the final response incorporates both the retrieved factual context and the generative capabilities of the model.