Retrieval-Augmented Generation (RAG) optimizes the output of a large language model (LLM) by referencing an authoritative knowledge base outside of its training data sources before it generates a response. This approach helps give the model current information and ground it in domain-specific or proprietary data. It also provides a controllable information source, which you can use to set access controls to specific content and troubleshoot issues in the responses.
RAG works by connecting a generator (often an LLM) to a content
database (such as a knowledge store) through a retriever. The retriever
is responsible for finding relevant information. In most enterprise applications, the
content database is a vector store, the retriever is an embedding model, and the generator
is an LLM. For more information, see Retrieval Augmented
Generation
A RAG system has several components. This guide focuses on how to use Amazon Nova as an LLM in any RAG system.
You can use Amazon Nova models as the LLM within a Text RAG system. With Amazon Nova models, you have the flexibility to build a RAG system with Amazon Bedrock Knowledge bases or build your own RAG system. You can also associate your knowledge base with an Agent in Amazon Bedrock Agents to add RAG capabilities to the Agent. For more information, see Automate tasks in your application using conversational agents.
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