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this post was submitted on 08 Aug 2025
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That's the thing about AI in general, it's really hard to "fix" issues, you maybe can try to train it out and hope for the best, but then you might play whack a mole as the attempt to fine tune to fix one issue might make others crop up. So you pretty much have to decide which problems are the most tolerable and largely accept them. You can apply alternative techniques to maybe catch egregious issues with strategies like a non-AI technique being applied to help stuff the prompt and influence the model to go a certain general direction (if it's LLM, other AI technologies don't have this option, but they aren't the ones getting crazy money right now anyway).
A traditional QA approach is frustratingly less applicable because you have to more often shrug and say "the attempt to fix it would be very expensive, not guaranteed to actually fix the precise issue, and risks creating even worse issues".