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Defensible Moats: Unlocking Enterprise Value with Large Language Models at QCon San Francisco
InfoQ - AI, ML & Data Engineering www.infoq.com
In a recent presentation at QConSFrancisco, Nischal HP discussed the challenges enterprises face when building LLM-powered applications using APIs alone. These challenges include data fragmentation, the absence of a shared business vocabulary, privacy concerns regarding data, and diverse objectives among stakeholders.
By Andrew Hoblitzellagents ai andrew apis applications building business challenges data diverse enterprise enterprises face fragmentation language language models large language large language models llm machine learning ml & data engineering presentation privacy qcon qcon san francisco 2023 san francisco stakeholders value