Debate: Should AI prototype tools fail fast or be held to higher standards before release?

I was testing an AI code generator last week for a side project (just a simple app for my neighbor's bakery in Austin). It cranked out 200 lines of Python in 30 seconds, but then it straight up missed a key function I asked for. I spent 2 hours fixing its mistakes. So which side do you fall on: do you let AI fail often and learn from mistakes (like a real dev), or do you expect near-perfect output right away? Has anyone else had an AI tool give you something that looked good but broke under pressure?

2 comments

2 Comments

skylerc861mo ago

and the scary part is the mistakes are usually in the exact places you least expect. the fake testimonial thing is wild because it looks so real on the surface, but that's kind of the problem. these tools are getting good enough to sound confident while being totally wrong, and that confidence makes you drop your guard. for code it's the same, the structure looks clean and the logic flows, but then the edge case you specifically mentioned just isn't there. i think the real issue is we're judging them like junior devs when they're more like that friend who's really enthusiastic but has no idea what they're doing yet. you gotta check everything they hand you, and that basically eats the time you thought you were saving.

kim.emma1mo ago

That 2 hour fix really stings, I totally get it. I've had an AI writing assistant give me a whole marketing email that looked perfect until I noticed it invented a fake customer testimonial. It's hard to trust the output when the mistakes are hidden deep in something that looks clean.

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