Generative AI has quickly become one of the most talked-about technologies of our time. From chatbots that write convincing essays to image generators that create stunning artwork, it feels as though these tools sprang out of nowhere, armed with limitless creativity. The spotlight naturally falls on the models themselves—the clever algorithms and neural networks that mimic human intelligence. But behind every polished prompt and every accurate, context-rich response lies an unsung hero: data engineering. Without the infrastructure, pipelines, and workflows that move and shape data, generative AI would remain little more than an idea on paper. The Model-Centric Myth When…
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