developer-roadmap/src/data/roadmaps/ai-engineer/content/rag@9JwWIK0Z2MK8-6EQQJsCO.md

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)?
• @article@What is Retrieval-Augmented Generation? Google
• @video@What is Retrieval-Augmented Generation? IBM