# Custom Retrieval Augmented Generation architectures on AWS
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The previous section describes how to use a fully managed AWS service for Retrieval Augmented Generation (RAG). However, some use cases require more control over the system components, such as the retriever or the LLM (also called the *generator*). For example, you might need the flexibility to choose your own vector database or access an unsupported data source. For these use cases, you can build a custom RAG architecture.

This section contains the following topics:
+ [Retrievers for RAG workflows](rag-custom-retrievers.md)
+ [Generators for RAG workflows](rag-custom-generators.md)

For more information about how to choose between the retriever and generator options in this section, see [Choosing a Retrieval Augmented Generation option on AWS](choosing-option.md) in this guide.