Explain Retrieval-Augmented Generation (RAG) in AI systems.

Retrieval- Augmented Generation (RAG) is a mixed method AI model that integrates information retrieval with generative AI models to enhance the accuracy and relevance of responses. Rather than just using the knowledge that is encoded in a language model, RAG uses an external source of knowledge (such as a database, document store or even the world wide web) in the first place to retrieve relevant documents or data.

Imagine it in the following way: the AI possesses two instruments-

• The tool that locates facts or context.
• A natural language generation writing tool.

Through combining these, the AI will be capable of generating answers based on actual data, addressing uncommon or expert queries, and minimizing hallucinations (inaccurate information) that otherwise could happen had the AI sole relied on its knowledge of the internal model.

Key points of RAG

• Retriever: Locates pertinent pieces of text within big datasets.
  
• Generator: Puts the retrieved text through the generator to generate coherent, context-responses
  
• Advantages: Increased accuracy, current information, and performance of domain-related tasks.

 

Again, in the case of a RAG-powered system, when you provide a query on a recent research paper, it can first retrieve the abstract or discoveries of that paper and then create a summary or explanation thereon instead of just using its existing knowledge base.