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this post was submitted on 21 Aug 2025
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So... I actually proposed a use case for NLP and LLMs in 2017. I don't actually know if it was used.
But the usecase was generating large sets of fake data that looked real enough for performance testing enterprise sized data transformations. That way we could skip a large portion of the risk associated with using actual customer data. We wouldn't have to generate the data beforehand, we could validate logic with it, and we could just plop it in the replica non-prodiction environment.
At the time we didn't have any LLMs. So it didn't go anywhere. But it's always funny when I see all this "LLMs can do x" because I always think about how my proposal was to use it... For fake data.