What does the Retrieval Augmented Generation (RAG) process do?

The Retrieval Augmented Generation (RAG) process is designed to combine generative AI with real-time or updated information. By grounding customer and company data in generative AI, RAG ensures that the AI's responses are informed by relevant, contextual data. This enhances the quality and accuracy of the generated outputs, making them not only coherent but also informative and aligned with the current state of knowledge and information.

In this context, it is essential for the generative model to access up-to-date information, allowing it to provide insights that are relevant and actionable rather than relying solely on static training data or historical context. This dynamic integration of data helps in crafting responses that reflect both customer-specific nuances and the latest company information, which is particularly valuable in the customer service and sales environments where Salesforce operates.