# 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.

Learn more from the following resources:

- [@article@What is Retrieval Augmented Generation (RAG)?](https://www.datacamp.com/blog/what-is-retrieval-augmented-generation-rag)
- [@article@What is Retrieval-Augmented Generation? Google](https://cloud.google.com/use-cases/retrieval-augmented-generation)
- [@video@What is Retrieval-Augmented Generation? IBM](https://www.youtube.com/watch?v=T-D1OfcDW1M)