Specifically, we used semantic similarity for shuffled answers (so they are close and not too easy), completely shuffled answers, and AI-generated distractors.

Specifically, we used semantic similarity for shuffled answers (so they are close and not too easy), completely shuffled answers, and AI-generated distractors. We are now going to choose the most popular default (based on retention during A/B/C testing), and also offer the mode as a choice. The quiz question (though not answer, since overwritten) from the demo you posted was generated in 2020, with T5 not GPT-3. Other and more complex questions were also possible with our finetuned tech on a bespoke carefully collected dataset. It was at the time much more impressive than it is now with GPT models, and we had it out before competitors even launched(the closest thing was old style GOFAI and a few online tutorials on finetuning T5 for similar tasks that had inferior data).