What Is Retrieval-Augmented Generation (RAG)?

RAG, or Retrieval-Augmented Generation, is a machine-learning approach that enhances Large Language Model (LLM) responses by retrieving source information from external data stores to augment generated responses. This technique allows for more accurate and contextual responses, reducing the limitations of standalone LLMs such as hallucinations, lack of explainability, and static training data. By using RAG applications, businesses can provide a personalized experience with domain-specific knowledge, increased accuracy, contextual understanding, explainability, and up-to-date information. Common use cases for RAG include customer support chatbots, business intelligence and analysis, healthcare assistance, legal research, and more.

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