Explanation
Correct Answer: B) Embeddings and Retrieval-Augmented Generation (RAG)
Why this is correct:
- Embeddings are used to convert text (like research papers) into numerical vectors that capture semantic meaning, enabling similarity searches.
- Retrieval-Augmented Generation (RAG) is specifically designed for this use case:
- Retrieval: Searches through uploaded documents (research papers) to find relevant information
- Augmented Generation: Uses the retrieved information to generate accurate, context-aware responses
- This prevents hallucinations and ensures responses are grounded in the actual research papers
Why other options are incorrect:
- A) Reinforcement Learning: Used for training models through reward-based feedback, not for document search and retrieval
- C) Few-shot prompting: Involves providing a few examples to guide model responses, but doesn't handle document search and retrieval
- D) Transfer Learning: Involves adapting pre-trained models to new tasks, but doesn't specifically address document search and information retrieval
Key AWS Bedrock Features for this use case:
- Knowledge Bases: Store and index documents (research papers)
- Embeddings: Convert text to vectors for semantic search
- RAG: Retrieve relevant content and generate responses based on that content
- Foundation Models: Use models like Claude or Llama for natural language understanding and generation
