Dive deep into the world of Generative AI and its potential for enterprise innovation. Learn the critical distinctions between Public and Private Large Language Models (LLMs) and how they can impact your organization’s AI initiatives.

Large language models (LLMs) continue to command a blazing bright spotlight, as the debut of ChatGPT captured the world’s imagination and made generative AI the most widely discussed technology in recent memory (apologies, metaverse). ChatGPT catapulted public LLMs onto the stage, and its iterations continue to rev up excitement—and more than a little apprehension—about the possibilities of generating content, code, and more with little more than a few prompts.

While individuals and smaller businesses consider how to brace for, and benefit from, the ubiquitous disruption that generative AI and LLMs promise, enterprises have concerns and a crucial decision to make all their own. Should enterprises opt to leverage a public LLM such as ChatGPT, or their own private one?

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Public vs private training data

ChatGPT is a public LLM, trained on vast troves of publicly-available online data. By processing vast quantities of data sourced from far and wide, public LLMs offer mostly accurate—and frequently impressive—results for just about any query or content creation task a user puts to it. Those results are also constantly improving via machine learning processes. Even so, pulling source data from the wild internet means that public LLM results can sometimes be wildly off base, and dangerously so