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How does RAG work in AI and why do we need it?
Retrieval-Augmented Generation is a hybrid approach that allows AI systems to generate responses by combining retrieved information from external sources with language models' generative capabilities. Traditional language models generate answers based solely on learned patterns within their training data. RAG enhances this process by explicitly retrieving relevant data from large document collections or knowledge bases to inform the generation process.
Written byAnnie Hayes
Published onNovember 4, 2025
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