Hallucinations have become a bit too much of a boogeyman.
One should not rely on LLMs as any sort of authoritative representation of training data where data integrity is critical.
But there's generally very little propensity for hallucination from in context information you are feeding into them live.
Additionally, even just a second pass with a fine tuned classifier checking for hallucinations between provided data and output can reduce the degree to which they occur significantly.
The low hanging fruit when the models first released of summarizing massive sets of training data is definitely an area where hallucinations have been a problem, but arguably the greater value in models moving forward is having turned them into informal logic engines of increasing caliber.
In that application, hallucinations are far less of a concern unless the context extensively overlaps with training data, and in those cases the hiccups can generally be effectively broken by replacing tokens with representative placeholders (such as if working with a LLM on a variation of the goat, wolf, and cabbage problem where it keeps hallucinating details from the normal form, using different nouns or using emojis in place of , , and ).
The issue of speed is much more salient, but I could definitely see LLMs in combination with the generative tech stacks coming up in 3D generation being used to help create large swaths of synthetic scenario data for edge cases less likely to occur and be captured in real world driving conditions, which would in turn train faster and more comprehensive self-driving models in vehicle.