AWS Certified AI Practitioner

Explanation

In a Retrieval-Augmented Generation (RAG) pipeline using Amazon Bedrock:

1. Embeddings are vector representations of text that capture semantic meaning.

2. The primary purpose of embeddings in RAG is to enable semantic search and retrieval of relevant documents or context from a knowledge base.

3. How it works:

   • Documents are converted into embeddings and stored in a vector database
   • When a query is received, it's also converted into an embedding
   • The system retrieves the most semantically similar documents (based on embedding similarity)
   • This retrieved context is then provided to the LLM for generation

4. Why other options are incorrect:

   • A) Train the model from scratch: RAG doesn't train models from scratch; it uses pre-trained foundation models
   • C) Generate long text responses directly: Generation is done by the LLM, not embeddings
   • D) Perform image captioning: Image captioning typically uses vision models, not text embeddings

5. Amazon Bedrock integration: Amazon Bedrock provides foundation models and embedding models that can be used to create embeddings for documents and queries in a RAG architecture.