A new technological term (and acronym) has found its way to artificial intelligence (AI). Retrieval-augmented generation (RAG) is a framework that combines two critical components of AI: information retrieval and generative models. It is designed to enhance the capabilities of AI systems by equipping them with the ability to look up relevant information and use that context to generate more accurate, insightful and contextually relevant responses. Tech Radar brought this interesting topic to our attention in their article, “What is RAG in AI? The low-down on Retrieval Augmented Generation.
At its core, RAG bridges the gap between retrieval-based systems (which excel at finding precise pieces of information) and generative systems (which are adept at creating natural language responses). By combining these two strengths, RAG systems can produce outputs that are both grounded in factual information and expressed in a coherent and creative manner.
While RAG offers significant advantages, it’s not without challenges. The system’s performance heavily depends on the quality and relevance of retrieved documents. Poor retrieval can lead to inaccurate or irrelevant responses.
In a world increasingly reliant on AI for decision-making and problem-solving, RAG is poised to play a pivotal role by ensuring that generative systems are not just creative but also grounded in reality.
The real challenge is that most organizations have little knowledge on how AI systems make decisions. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Data Harmony is a fully customizable suite of software products designed to maximize precise and efficient information management and retrieval.
Melody K. Smith
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