What are the steps involved in the Retrieval-Augmented Generation (RAG) process?

The Retrieval-Augmented Generation (RAG) process is designed to enhance text generation by incorporating relevant information retrieved from a database or knowledge source. The correct steps are Retrieval, Augmentation, and Generation.

In this context, Retrieval refers to the process of fetching potentially useful information or documents from an external source. This step is crucial because the quality and relevance of the retrieved data directly influence the output generated later. Augmentation involves processing the retrieved information to integrate or contextualize it with the input query. This ensures that the generated response is not only coherent but also enriched by the specific knowledge identified during the retrieval phase. Finally, Generation is the step where the system produces a text response, leveraging both the original query and the augmented information gathered through the previous steps.

This structured sequence highlights the synergy between data retrieval and natural language generation, allowing for more informed and contextually relevant outputs in generative AI applications.