This is not how modern training pipelines work. The models are trained on proprietary alignments that ensure factuality.
Regardless, that random dude from a local accelerator probably regurgitates those blog articles himself.
Just like reviews "ensure" correctness on Wikipedia - which is actually a good case. In my childhood teachers would make us avoid Wikipedia. Now they advice students to use it.
Most of the wrong thing it says is just invented by the model, not things it has seen, cleaning up the input doesn't add much there since it writes the same kind of bullshit as bullshitting humans do.
> The software engineer needs to "de-hallucinate" him in order to get a proper answer.
The question is not whether a model is sound and complete. The question how LLMs perform in comparison to the existing solutions.
Don't let perfect get in the way of good.
Let's take an example. I studied physics and can likely answer most questions on nuclear physics. However, as it is a while back, my answers will not sound as smooth, and I may have to correct myself a few times.
If you ask ChatGPT the same questions, it will come up with elegant, convincing answers. As a layman, you will likely prefer what ChatGPT comes up with, as it is answers are eloquent. Of course, until you build a nuclear reactor using ChatGPT knowledge. Once the fuel rods become critical and start making its way through the earth's crust, you may realise you should have relied on my stumbling answers instead.