Explanation

Retrieval-Augmented Generation (RAG) is the prompting method being used in this scenario. Here's why:

Key Concepts:

1. Hallucinations in LLMs: Large Language Models can sometimes generate plausible-sounding but incorrect or fabricated information.
2. Retrieval-Augmented Generation (RAG): This approach combines:
   • Retrieval: Fetching relevant information from external knowledge bases
   • Augmentation: Adding this retrieved information to the prompt
   • Generation: Using the augmented prompt to generate more accurate responses

Why Option B is Correct:

• The developer is specifically adding external facts retrieved from a knowledge base into the prompt
• This is the core mechanism of RAG - enhancing prompts with retrieved information to improve accuracy
• RAG is particularly effective for reducing hallucinations because it grounds the model's responses in factual, verifiable information

Other Options Explained:

• A. Chain-of-thought prompting: Involves breaking down complex problems into intermediate reasoning steps, not about adding external facts
• C. Zero-shot prompting: Asking the model to perform tasks without any examples, not about retrieving external information
• D. Self-evaluation prompting: Having the model evaluate its own responses, not about incorporating external knowledge

AWS Bedrock Context:

In AWS Bedrock, RAG can be implemented using:

• Knowledge Bases for Amazon Bedrock
• Vector databases for storing and retrieving information
• Embedding models to convert text into vector representations
• Retrieval mechanisms to find relevant information

This approach ensures that the model has access to up-to-date, domain-specific information, significantly reducing the likelihood of hallucinations.