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Disagree, someone like the other commenter who points out LLMs don't even understand the domain concepts correctly versus someone who uses it anyways for corporate proprietary results have very different standards for what is acceptable. If you wrangle an LLM with harnesses and clever prompts you could use it to get some amazing results but that has more to do with trial and error and creativity, not some kind of fundamental skill of using LLMs.
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It definitely understands the concepts well enough if you give it the right context. I'm not the only one saying this either. Like I said, it's a skill issue.
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That's the Clever Hans argument, and the fact that you confidently use this unfalsifiable tactic ("Give it just the right context and it understands stuff!! It works!!" (Well, until the next iteration and then the next until the system paints itself into a corner)) tells me you are engaging in broscience / pseudoscience. Like I say, anti-scientific attitudes like yours are part of the problem, fanning the hype. It's bad faith to attribute people's criticisms of LLMs as some kind of lack of skill. People on here, many who are actual scientists and professional programmers, are very intelligent and highly trained, if they wanted to play around with LLMs they very likely capable of getting impressive one-time results, but proper, sustained use in a non-"vibe-coding" manner, such as with guarantees for validity, consistency, replicability, extensibility, and so forth is a completely open problem. Therefore it is out of proportion to reduce that to human skill. It's analogous to framing a bad design pattern as user error--disingenuous and bad faith. Ironically, with an intellectual standard like that, it then becomes easy to become overconfident about LLMs.
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