Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a method that combines the strengths of information retrieval and language generation. When asked a question, RAG first searches an external knowledge base or documents to find relevant information. Then, it uses this retrieved data to generate a more accurate and informed response. This approach helps the AI provide answers that are both contextually relevant and grounded in factual data, making its responses more reliable, especially for topics requiring specific or up-to-date knowledge.