Answer: Contextual / Retrieval-Augmented Prompting (RAG)

## Explanation This scenario describes **Retrieval-Augmented Generation (RAG)**, which is a contextual prompting approach. Here's why: ### Key Characteristics of RAG: - **External Knowledge Retrieval**: The system retrieves relevant information from an external source (S3 bucket containing company handbook) - **Context Integration**: Retrieved policy paragraphs are included in the prompt before generating the answer - **Dynamic Context**: The model uses up-to-date, specific information rather than relying solely on its pre-trained knowledge ### Comparison with Other Options: - **A) Few-shot Prompting**: Involves providing examples in the prompt, not retrieving external documents - **C) Zero-shot Prompting**: No examples or external context provided - just the question - **D) Chain-of-Thought Prompting**: Focuses on step-by-step reasoning, not document retrieval ### Why RAG is Ideal for This Use Case: 1. **Accuracy**: Ensures answers are based on the latest company policies 2. **Scalability**: Can handle large document repositories 3. **Maintainability**: Policies can be updated in S3 without retraining the model 4. **Transparency**: Shows which specific policy sections were used for the answer This approach is particularly valuable for HR systems where policy accuracy and compliance are critical.

Author: Ritesh Yadav