AWS Certified Cloud Practitioner

Explanation

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

• Embeddings are vector representations of text that capture semantic meaning

• They are used to retrieve relevant context or documents from a knowledge base before the generation step

• The retrieval process works by:

  1. Converting query text into embeddings

  2. Finding the most similar document embeddings in a vector database

  3. Passing the retrieved relevant context to the language model for generation

Why other options are incorrect:

• A: RAG doesn't train models from scratch - it uses pre-trained foundation models

• C: Embeddings don't generate text directly - they're used for retrieval, while the language model handles generation

• D: This question is about text-based RAG, not image captioning

This approach enhances generation by providing factual, up-to-date context while leveraging the language model's reasoning capabilities.