As developers building in the AI ad space, we often obsess over the Generation part. We tweak diffusion models and LLMs to produce stunning visuals and snappy copy. But there’s a silent killer in the workflow: Creative Fatigue and Compliance Risk . If you are programmatically pushing AI-generated content directly to the Meta API without a rigorous QA layer, you are risking two things: Budget Burn : Ads that look "too AI" and fail to convert. Account Bans : Content that accidentally trips Meta's sensitive policy triggers. Here is how I built an automated Peer-Review layer into AI Ad Generator to solve this. 1. The "Ad-Native" Logic Gate A pretty ad is useless if it doesn't follow direct-response psychology. My QA engine doesn't just check for grammar; it scores the content based on Retention Logic . Before any asset is finalized, it passes through a secondary LLM agent (the "Reviewer") with a specific persona: The Cynical Media Buyer .…