Answer: Retrieve relevant context or documents before generation

## Explanation In a Retrieval-Augmented Generation (RAG) pipeline using Amazon Bedrock, embeddings play a crucial role in the retrieval phase: 1. **How RAG works**: - Documents are converted into vector embeddings (numerical representations) - These embeddings are stored in a vector database - When a query is received, it's also converted into an embedding - The system retrieves the most relevant documents by finding embeddings similar to the query embedding - Retrieved context is then fed to the language model for generation 2. **Why option B is correct**: - Embeddings enable semantic search to find relevant context/documents - This retrieval happens BEFORE the generation phase - The retrieved context enhances the model's response with relevant information 3. **Why other options are incorrect**: - **A**: RAG doesn't train models from scratch; it uses pre-trained models - **C**: Embeddings don't generate text directly; they help retrieve context for the generation model - **D**: Image captioning is a different task; RAG typically focuses on text-based retrieval and generation 4. **Amazon Bedrock context**: - Amazon Bedrock provides foundation models and tools for building RAG applications - It includes embedding models (like Amazon Titan Embeddings) to convert text into vectors - These embeddings are used with vector databases (like Amazon OpenSearch, Pinecone, etc.) for efficient retrieval

Author: Ritesh Yadav