A company has built a customer support chatbot using a large language model (LLM). While the LLM is good at general conversation, it often provides generic answers or fails to answer questions about the company's very specific and newly updated product specifications. The company has an extensive, up-to-date knowledge base containing all product details. How could Retrieval-Augmented Generation (RAG) primarily help improve the chatbot's responses in this scenario?