A company’s large learning model (LLM) is producing hallucinations that are a result of the Knowledge cutoff. How does retrieval-augmented generation (RAG) overcome this limitation?
A.
RAG fine-tunes the LLM on specific customer query patterns to improve the speed and efficiency of response generation.
B.
RAG enhances the creative writing capabilities of the LLM to generate more engaging and informative responses.
C.
RAG enables the LLM to retrieve relevant and up-to-date information from knowledge sources.
D.
RAG uses human oversight to ensure accuracy before presenting information to the customer.
The primary purpose of RAG is to address the "knowledge cutoff" and hallucination issues of LLMs. It does this by retrieving relevant, up-to-date information from external knowledge sources (like databases or documents) at inference time and then using this retrieved information to ground the LLM's generation, ensuring factual accuracy and relevance to the specific query.
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