RAG (Retrieval-Augmented Generation) connects large language models to real-time data sources, significantly reducing hallucinations and improving factual accuracy. This shift transforms AI systems into end-to-end pipelines, making data engineering as crucial as model development for enterprise applications. Developers now focus on integrating fresh, private data into AI systems, enabling more reliable and contextually relevant responses.
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