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From a coding perspective, proper technical systems already have checks and balances (e.g. test cases) to catch bad code, and is something that's important to have regardless of generative AI usage.

From a creative/informational perspective, there are stories every day of hallucinations and the tech companies are correctly dunked on because of it. That's more product management error than AI error.

AI hallucination isn't a showstopper issue, it just has to be worked around.



The fact that every AI-based company gets dunked-on for hallucination somewhat suggests that hallucination is a showstopper issue and in fact cannot be worked around.


I agree personally. I don't use LLMs for most things these days because I've been bitten in the ass enough times (whether it was real fallout or just me being able to prove the LLM wrong) that I don't trust them at all. To me it's a showstopper because it can get even the simplest explanations very incorrect and that's not useful to me.

They're still my first go over google these days but I usually only use them for code or weird word transformations and any information is double checked. Pretty useless as an answer bot.


A "showstopper" issue is a QA term-of-art indicating an issue that the project cannot be pushed to production as long as it exists. The AI project managers made the calculation that the proportion of hallucination issues (and their consequences) is within acceptable bounds.

The tide is only turning recently on whether that's a good business tradeoff.


Nothing is a showstopper, if your quality-control is poor enough. The value of AI in industries with high-quality product management is inversely proportional to the quality of human input. In most well-paying careers, that makes AI obsolete from the get-go.


Where "acceptable bounds" equals whatever the current proportion is.


There's no "hallucination". This word is simply PR spin to disguise faulty output.




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