Software & Technology · Glossary
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) is a technique that grounds a large language model's answers in retrieved documents from a trusted knowledge source, rather than relying only on the model's trained parameters. It reduces hallucination and lets models cite current, proprietary information.
RAG is the dominant pattern for enterprise AI applications because it lets a general model answer accurately about a specific company's data without retraining. It pairs a vector database for retrieval with a generation model, and its quality hinges on retrieval relevance, chunking, and source freshness.
In practice
In the software and technology industry, retrieval-augmented generation (RAG) is used by product managers and data scientists to enhance customer support chatbots and knowledge management systems. By integrating real-time data retrieval with AI-generated responses, teams can provide accurate and contextually relevant information, driving faster resolution of customer inquiries. This capability not only improves user satisfaction but also streamlines operations and reduces costs, making RAG a valuable asset for companies competing in a data-driven market.
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