An approach in machine learning that enhances the process of generating text by dynamically incorporating information retrieved from a large dataset or knowledge base during the generation process. Instead of relying solely on pre-learned patterns from the training data, RAG systems query a document database to find relevant information that can be used to inform the response. This retrieved information is then integrated into the generation process, allowing the model to produce more accurate, relevant, and detailed outputs based on up-to-date or specific knowledge that was not necessarily present in its original training data. RAG combines the strengths of neural language models with the vast informational resources available in external databases, enabling applications like more informed chatbots, improved search engines, and enhanced content creation tools.