What is Retrieval Augmented Generation? How it Works & Use Cases

Retrieval-augmented generation, or RAG, is a technique for enhancing the output of large language models by incorporating information from external knowledge bases or sources.

By retrieving relevant data or documents before generating a response, RAG improves the generated text’s accuracy, reliability, and informativeness. This approach helps ground the generated content in external sources of information, ensuring that the output is more contextually relevant and factually accurate.

Read on to learn more about RAG...