RAG enables you to combine your vector store with the LLM itself. This combination allows the LLM to reason not just on its own pre-existing knowledge but also on the actual knowledge you provide through specific prompts. This process results in more accurate and contextually relevant answers.
RAG empowers organizations to harness the full potential of their data, providing a more efficient and accurate way to interact with AI-driven solutions.
Traditional LLMs are trained on vast datasets often called "world knowledge." However, this generic training data is not always applicable to specific business contexts.
#generative-ai #retrieval-augmented-generation #large-language-models #business-applications #knowledge-management
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