Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources. It is crucial for ensuring the quality, accuracy, and relevance of the generated output.


Retrieval-Augmented Generation (RAG) combines two main components: