What is the purpose of Retrieval Augmented Generation (RAG) in text generation?
Retrieval-Augmented Generation (RAG) combines retrieval mechanisms with text generation, allowing models to pull external knowledge before generating responses.
How RAG Works:
The model retrieves relevant documents from an external database.
Uses this retrieved information to generate factually grounded responses.
Reduces hallucinations, improving accuracy and context relevance.
Why Other Options Are Incorrect:
(A) is incorrect because RAG modifies the retrieved text by integrating it into a generated response.
(B) is incorrect because RAG retrieves and uses data, not just stores it.
(C) is incorrect because RAG relies on external knowledge, whereas LLMs alone use internal pre-trained knowledge.
Oracle Generative AI Reference:
Oracle AI applies RAG techniques to improve enterprise AI applications, enhancing fact-based text generation.
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