12 Considerations for Using Retrieval-Augmented Generation in Your Processes

Retrieval-augmented generation (RAG) is an advanced technique within generative AI that combines two functions: retrieving relevant information and generating new content.

This approach enhances the capabilities of generative models by integrating a retrieval mechanism that fetches relevant information from a pre-existing database. This information then informs the generative model, resulting in more accurate, contextually relevant, and informative outputs.

Download this infographic to learn the top considerations when using RAG, which are essential to ensuring the quality of the retrieval data and maintaining the accuracy of the generated content.