Retrieval Augmented Generation (RAG) significantly improves customer interaction for e-commerce businesses by integrating Large Language Models (LLMs) with customized knowledge bases. Traditional LLMs, while powerful, can provide hallucinated or outdated responses and lack specific domain knowledge. This can frustrate customers seeking information about product features or issues. RAG addresses these challenges by allowing LLMs to access relevant, up-to-date information, ensuring accurate and timely responses, and ultimately reducing support tickets and enhancing customer satisfaction.
Retrieval Augmented Generation (RAG) combines the power of Large Language Models (LLMs) with a custom knowledge base, enabling precise and contextually relevant responses from customers.
Traditional LLMs like GPT-4 have their knowledge 'frozen' at the last training data, leading to issues like hallucinations, outdated information, and lack of specificity.
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