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Enterprise AI: Beyond the Hype of Generative AI and LLMs : r/dataengineering

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News & discussion on Data Engineering topics, including but not limited to: data pipelines, databases, data formats, storage, data modeling, data governance, cleansing, NoSQL, distributed systems, streaming, batch, Big Data, and workflow engines. I work as a data engineering consultant, surrounded by sales-driven consultants eager to ride the wave of hype by building, frankly, trivial Proofs of Concept (PoCs) using the latest features from various vendors and tools. Recently, this hype has centered around Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and similar technologies. While jumping on the hype train may provide short-term satisfaction, I prefer to engage in deep and meaningful work. LLMs have significantly advanced the field of language processing. However, do LLMs and Generative AI address all the AI challenges businesses face? To be clear, I'm questioning whether they solve all AI-related problems for businesses, not all business problems.

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