I’ve spent enough time around AI projects this year to realize that some lessons only show up after you’ve built something and put it in front of real users. One thing I kept running into was assuming a model problem was a model problem. More often than not, the root cause ended up being data quality, retrieval, evaluation, or the workflow around the model. And the expensive mistakes seem to be the ones that look obvious in hindsight. For people building and deploying AI systems, what lesson took you the longest to learn? What assumption turned out to be completely wrong once you had real experience with it? submitted by /u/Financial_Ad_7297
Originally posted by u/Financial_Ad_7297 on r/ArtificialInteligence
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