RAG
Retrieval-Augmented Generation (RAG)
What is Retrieval-Augmented Generation?
It can be described as an artificial intelligence approach that enables LLMs to go beyond the knowledge acquired during training by retrieving relevant content from external information sources and grounding their responses in that content. In this approach, the retrieval component identifies relevant content in knowledge bases, documents, databases, or enterprise repositories and provides it to the LLM as context. The large language model can use the retrieved information to generate more accurate, up-to-date, and contextually relevant responses. In short, Retrieval-Augmented Generation helps reduce the likelihood of the model generating inaccurate or outdated responses; however, it does not eliminate this risk entirely.
Retrieval-Augmented Generation Use Cases
Retrieval-Augmented Generation is generally referred to in Turkish as "Bilgi Getirme Destekli Üretim"; however, the acronym RAG is widely used across the industry. Common use cases for Retrieval-Augmented Generation include enterprise knowledge management, customer service, document search, chatbots, and knowledge assistants. For example, rather than providing customers with generic responses, chatbots can use the RAG approach to search internal product manuals, frequently asked questions (FAQ) documents, and support records to generate more contextually relevant responses. This approach can also be used in healthcare. Up-to-date medical research, pharmaceutical information, and authorized patient data can be integrated into the system to support healthcare professionals with information retrieval and decision support during diagnosis and treatment processes.
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