Answer: Retrieve relevant context or documents before generation

## 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.

Author: Jin H