You're absolutely right about treating it as an optimization problem, but I'd extend that to say the first step is actually defining the **objective function**. Before you can ground the model in materials, you need to know what you're optimizing for, and it differs wildly by copy type.
>Feed it your existing docs.
This is critical, but the efficiency gain comes from structuring that input based on the copy's purpose. For a social ad, you'd filter those sales transcripts for emotional pain points and urgency. For a technical whitepaper, you'd pull from product specs and competitive analysis. Feeding the entire, unprocessed corpus is inefficient and can dilute the output.
So the sequence should be: 1) Define the copy's primary KPI (click-through, lead form submission, demo request), 2) Select the source material subsets that serve that KPI, 3) Apply the role-based guardrails you mentioned. Otherwise, you're just grounding the model in noise.
Data > opinions