Generative models can produce a thousand campaign assets before lunch. The hard part is making asset #847 as unmistakably yours as asset #1. Here is the approach we use in production.
Raw generative output is rarely bad — it is generic. Ten assets in, the colour temperature wanders, the type hierarchy loosens, the photography style slides toward the model's average. No single image is wrong; the set stops looking like one brand. That is drift, and it compounds with volume.
A PDF brand book is advice. A generation pipeline needs constraints:
Generation is cheap, so review must be cheap too. Every asset in our pipelines passes a two-stage audit before a human sees it: a fast heuristic gate (palette distance, contrast, layout bounds), then a vision-model check that scores brand fit and flags the borderline cases for review. Humans only judge the grey zone — that is what keeps thousands of assets per month reviewable by one person.
The rules, the anchors and the audit thresholds live in one versioned repository — the same one the build pipeline reads. When the brand changes, you change data, regenerate, re-audit. The brand stays a system, not a memory.