LLMS Course | Build vs Buy |How to Decide
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Advantage of using RAGBetter Accuracy: If factual correctness is crucial, RAG can be fantastic. It retrieves information from external sources, allowing the AI assistant to double-check its responses and provide well-sourced answers. Domain Knowledge: Imagine an AI assistant for medical diagnosis or legal or up to date tax laws. RAG can access medical databases to enhance its responses and ensure they align with established medical knowledge. Reduce Hallucination: LLMs can sometimes fabricate information, a phenomenon called hallucination in which they make up things. RAG mitigates this risk by grounding the response in retrieved data. Building Trust: By citing sources, RAG fosters trust with users. Users can verify the information and see the reasoning behind the response. Disadvantages of using RAGSpeed is Crucial: RAG involves retrieving information, which can add a slight delay to the response. If real-time response is essential, a pre-trained LLM might be sufficient. Limited Context: RAG works best when the user's query and context are clear. If the conversation is ambiguous, retrieved information might not be relevant. Privacy Concerns: If the AI assistant deals with sensitive user data, RAG might raise privacy concerns. External retrievals could potentially expose user information. |
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