RAG

Retrieval-Augmented Generation (RAG) is an AI approach that combines information retrieval with language generation to create more accurate, contextually relevant outputs. It works by first retrieving relevant data from a knowledge base or external source, then using a language model to generate a response based on that information. This method enhances the accuracy of generative models by grounding their outputs in real-world data, making RAG ideal for tasks like question answering, summarization, and chatbots that require reliable, up-to-date information.

Visit the following resources to learn more:

• @article@What is Retrieval-Augmented Generation? - Google
• @article@RAG Explained: Understanding Embeddings, Similarity, and Retrieval
• @video@What is Retrieval-Augmented Generation? - IBM