The instinct is to hit regenerate and hope the dice land better. Don’t. Bad output is almost always a prompt problem, and the symptom tells you which part of the instruction was thin.
A prompt has five parts: Role, Context, Task, Format, and Constraints (see the prompt builder). A failed draft usually points straight at one of them. Generic output means thin context. Hallucination means no source limit. A robot voice means no tone sample. Missed requirements mean you summarized instead of pasting the RFP. Read the symptom, fix that part, and move on.
Pick what the draft did wrong:
Pick the symptom. Bad output is a prompt problem, not a model problem — one good edit beats five retries.
Regenerating gambles on the model’s variance: the same thin prompt, rerolled. You might get a different draft, but it’s wrong in a new way and you’ve learned nothing. Changing the instruction changes the result on purpose. One good edit does more than five hopeful retries, and it makes the next prompt better too.
Find your last bad AI output, name the symptom above, apply the one fix, and re-run once. Then run the result through PARC before it touches the document.
We run the tooling under a human’s hand, so the draft that reaches you already reads like a person wrote it, because one did.
Book a call →