Taming hallucination and bad output

The model will sometimes invent an API that doesn't exist, call a function with the wrong signature or confidently delete the wrong block. You can't make a probabilistic system deterministic, so you design the system around it being wrong sometimes.

Onsite prompts in this loop have included addressing hallucinations in deployed models, so a vague "we'd improve the prompt" won't survive. Come with a layered playbook that reduces wrong output, catches what slips through and makes the rest cheap to reject.