There is a very small subset of business problems that are best solved by machine learning; most of them just need good data and an understanding of what it means.
We have collectively gotten so far ahead of ourselves with generative AI that we're setting ourselves up for failure. People dabble with ChatGPT for an afternoon and expect it to be able to resolve their supply chain issues or customer support questions.
Because you've put in such little effort into data preparation, it's very easy to get pleasantly surprised by initial results, she says, which then propels the next stage of experimentation, also known as prompt engineering.
Rather than do the hard, dirty work of data preparation, with all the testing and retraining to get a model to yield even remotely useful results, people are jumping straight to dessert, as it were.
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