Answer: Contextual / Retrieval-Augmented Prompting (RAG)

## Explanation This scenario describes **Retrieval-Augmented Generation (RAG)**, which is a contextual prompting approach where: 1. **Retrieval Phase**: The system retrieves relevant information from an external knowledge source (in this case, the company handbook stored in an S3 bucket) 2. **Augmentation Phase**: The retrieved relevant policy paragraphs are included in the prompt as context 3. **Generation Phase**: The model generates an answer based on the provided context **Why other options are incorrect:** - **A) Few-shot Prompting**: This involves providing examples of input-output pairs in the prompt, not retrieving external documents - **C) Zero-shot Prompting**: This involves asking the model to perform a task without any examples or retrieved context - **D) Chain-of-Thought Prompting**: This involves breaking down complex problems into intermediate reasoning steps, not retrieving external documents **Key characteristics of RAG:** - Uses external knowledge sources - Retrieves relevant information at inference time - Provides retrieved context in the prompt - Particularly useful for domain-specific knowledge that may not be in the model's training data

Author: Jin H