Retrieval-Augmented Generation (RAG) addresses limitations of large language models by retrieving relevant external information at runtime, enhancing accuracy and reliability. This approach reduces hallucinations and improves efficiency in handling private or updated data, making it crucial for developers building reliable AI systems. Watch for increased adoption of RAG in enterprise applications to enhance model performance.
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