Answer: Retrieve relevant context or documents before generation

## 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.

Author: Ritesh Yadav