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Retrieval Augmented Generation (RAG) is a framework designed to enhance the accuracy of AI responses by connecting them to real-time data sources. This approach mitigates the issue of AI hallucinations, where models generate plausible but incorrect information. By integrating retrieval and generation, RAG transforms AI from a creative storyteller into a reliable information provider.
RAG has been a game-changer since its introduction by Facebook AI in 2020, allowing AI systems to access live data without frequent retraining. Its applications range from customer service chatbots to content creation, making AI interactions more relevant and precise. As RAG continues to evolve, it promises to redefine how AI systems operate across various industries.
• RAG connects AI to real-time data for accurate responses.
• RAG enhances AI reliability by reducing hallucinations.
RAG combines generative AI with real-time data retrieval to improve response accuracy.
AI hallucination refers to the generation of plausible but incorrect information by AI models.
LLMs are AI systems that generate human-like text based on training data and context.
Meta AI, formerly Facebook AI, introduced RAG to enhance AI's access to real-time data.
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