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RAG: Why Your LLM Needs a Reality Check (and How to Fix It)
- LLMs like GPT-4 lack real-time data context, leading to inaccuracies in responses.
- RAG (Retrieval-Augmented Generation) bridges the gap between LLMs and real-world accuracy.
- RAG uses relevant data to enhance LLM responses and reduce fabrications by 60-80%.
- RAG is ideal for domain-specific accuracy and situations with constantly changing data.
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