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