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A university wants to use Amazon Bedrock to power a chatbot that answers questions based on uploaded research papers. The chatbot must search relevant information and generate responses. Which Bedrock capability supports this?
Real Exam
Community
RRitesh
0
A
Reinforcement Learning
B
Embeddings and Retrieval-Augmented Generation (RAG)
C
Few-shot prompting
D
Transfer Learning
Explanation:
Explanation
Embeddings and Retrieval-Augmented Generation (RAG) is the correct answer because:
- Embeddings convert text (like research papers) into numerical representations that can be efficiently searched and compared
- Retrieval-Augmented Generation (RAG) enables the system to:
- Retrieve relevant information from the uploaded research papers based on the user's question
- Augment the language model's knowledge with this retrieved context
- Generate accurate responses that are grounded in the specific research content
This approach is ideal for the university's use case because:
- It allows the chatbot to search through uploaded research papers to find relevant information
- It generates responses that are specifically based on the content of those papers
- It avoids the limitations of the base model's general knowledge by grounding responses in the provided documents
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: Provides examples to guide model responses but doesn't involve searching through external documents
- D) Transfer Learning: Involves adapting pre-trained models to new tasks, but doesn't specifically address document retrieval and generation
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