RAG is a framework that improves the quality of LLM outputs by giving the model access to a database while answering a prompt. The database, being a curated and trusted body of data, allows the model to incorporate up-to-date and reliable information into its responses. This approach is best suited for AI applications requiring additional contextual information, such as customer support responses or semantic search in an enterprise communication platform.