Score every AI output. That’s the job now.

The model drafts faster than any writer you’ve managed. It’s also wrong in quiet, plausible ways a color team won’t catch until page 40: an invented certification, a metric that sounds right, a product you don’t sell. In a proposal that isn’t a typo. It’s a compliance problem, and sometimes a legal one.

So the high-value work is no longer producing the words. It’s judging them before they reach the document. That’s a discipline, and a discipline needs a checklist.

Persuasive:does it connect features to the customer’s needs and carry the win themes, or just describe what you do? Accurate: is every fact verifiable, with no fabricated certs, metrics, or contract names? Relevant: does it answer this RFP in its terminology, or could it sit in any proposal? Complete: is every sub-requirement addressed, with evidence and no placeholders? Score each one to five. Anything under three goes back.

Pull up a section a model just drafted and rate it. The verdict updates as you go.

Keeping a human in the loop to measure and manage AI output is the spine of every serious AI-governance framework, including NIST’s AI Risk Management Framework. Its MEASURE and MANAGE functions assume someone is checking the output. PARC is that principle made specific to a proposal: four questions, scored, every time, before anything ships. The tooling generates. You stay accountable for what goes out under your name.