So what exactly is RAG? In simple terms, it stands for retrieval-augmented generation. Let us focus on these two aspects: retrieval and generation. With standard generative AI (GenAI), you provide a prompt, and a GenAI application would use a large language model to come up with a suitable response for the prompt. Now, imagine an application that can retrieve information from various sources and then generate a response based on the retrieved information. That is exactly how a RAG GenAI works. It provides context to the generated example. Let us explore this further with an example. If we ask something like "What is the best way to back up my customer database?" to a GenAI application, it would probably respond with some generic stuff. I would not know the details of the customer database that I am talking about. Now, suppose I have a design document with all the details. It has a section on data stores and explicitly lists out the customer database that is hosted on Amazon DynamoDB. The design document is uploaded to my organization’s SharePoint. So, the application will first do a retrieval of contextual information from SharePoint, augment the prompt with retrieved information, and then generate a response based on that. In this case, the application will provide strategies for backing up a DynamoDB database and direct me to the relevant sections in my design document.