Doing a loop and having the model review its own earlier output tends to solve this stuff, and for code requiring good test coverage catches them before they even hit that phase.

So while you can't remove "hallucinations" they're corrected before the process is complete, albeit at the cost of more tokens for verification.

Error percentages compound. Having an LLM do something with a 5% hallucination rate, then having an LLM (same or different) with a 5% error rate check it means there's a 10% chance of error, not .25%. Prompting can't save you here; it's the fundamental math.