It's fine, the LLM AIs we have now are just fancy versions of autocorrect. They, and other LMs, guess at statistically probable words/datapoints, and because th

It's fine, the LLM AIs we have now are just fancy versions of autocorrect. They, and other LMs, guess at statistically probable words/datapoints, and because they don't understand context, you might need to put your thumb on the scales to make the output actually be useful. They're at best very janky tools as soon as you're working with things that require context that isn't easily contained in some kind of confined area of work. Currently we are seeing the phenomenon 'habsburg AI' where AI's consume their own outputs as training data, which rapidly deteriorates their ability to actually be useful for much of anything. The thing is that there literally isn't enough human-made data to keep feeding them (they already ate the entire internet), so if you both want to continue ramping their intake of data and you also don't want them to get rapidly weird and completely useless, you pretty much have to get in there with elbow grease. Removing or deprioritizing data that's tripping up the model is one of the few ways you can do human-assisted refinement of these things. The sooner we all face the music that these things aren't magical truth machines, have a long way to go and there is no guaranteed rate of growth, the sooner this hype cycle can end.