What does RAG stand for in the context of NLP?

In the context of Natural Language Processing (NLP), RAG stands for Retrieval-Augmented Generation. This approach combines two critical components: retrieval, which involves sourcing relevant information from a knowledge base, and generation, where a model produces coherent and contextually relevant text based on that retrieved information.

This method is particularly effective for tasks such as question-answering, where a model needs to draw upon external information to provide accurate and informative responses. By leveraging retrieval systems, RAG can enhance the model's performance by grounding its generative capabilities in real-world data, resulting in answers that are both informative and contextually appropriate.

When RAG is employed, the model can access and utilize valuable information that may not be part of its initial training, thereby improving the quality and relevance of the generated text. This contrasts with approaches that rely solely on either generation or retrieval in isolation, as RAG effectively integrates both elements to create more robust NLP applications.