Building RAG with Knowledge Bases for Amazon Bedrock

RAG is a technique used in generative AI models to provide additional context and knowledge to foundational models during the generation process. It works by first retrieving relevant information from a knowledge base or corpus of documents, and then using this retrieved information to augment the input to the generative model.

RAG is a good choice for giving context to generative AI models because it allows the model to access and utilize external knowledge sources, which can significantly improve the quality, accuracy, and relevance of the generated output. Without RAG, the model would be limited to the knowledge and patterns it learned during training, which may not always be sufficient or up to date, especially for domain-specific or rapidly evolving topics.

One of the key advantages of RAG is that it enables the model to leverage large knowledge bases or document collections, which would be impractical or impossible to...