Retrieval Augmented Generation

Definition ∞ Retrieval augmented generation is a technique that enhances the output of language models by incorporating external data sources. It combines the generative capabilities of AI with a retrieval system that fetches relevant information before formulating a response. This process allows for more accurate, up-to-date, and contextually grounded text generation.
Context ∞ The application of retrieval augmented generation (RAG) in the cryptocurrency domain is gaining traction, particularly for analyzing complex financial data and generating informative summaries of market events. News coverage may discuss how RAG-powered tools can provide more precise insights into blockchain transactions or project documentation. The ability to ground AI responses in verifiable data is critical for building trust in AI-generated content within this sector.