Question
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Explanation
Statement 1 (No): RAG and fine-tuning are separate approaches. RAG adds retrieved content to the prompt, while fine-tuning is used to change a model's behavior, style, or task performance, so RAG does not require it.
Statement 2 (Yes): A model only knows the data available when it was trained, and RAG is the recommended pattern for frequently changing information. Responses are grounded in retrieved content and can include citations back to the source, which makes them verifiable.
Statement 3 (Yes): RAG retrieves relevant passages from your own documents and supplies them to the model as grounding data. The base model is unchanged, so no retraining is needed to get answers based on your organization's content.
