LLM-Generated Copy at Scale: Quality Controls That Work

Our quality control framework consists of five layers: brand voice scoring, fact-checking, compliance screening, human-in-the-loop review, and A/B performance gates.

Brand voice scoring uses embedding similarity to compare generated copy against a corpus of approved, high-performing copy from that customer. Variants that score below a cosine similarity threshold of 0.78 are automatically regenerated with tighter constraints.

Fact-checking runs the generated copy against the customer's product data API. If the copy references a price, a feature name, or a date, the system verifies it exists and is current before the message is queued. This layer alone catches approximately 3% of all generations in production.

Compliance screening flags copy containing prohibited phrases under GDPR, CAN-SPAM, and customer-specific brand guidelines. The flagged content is routed to a human reviewer rather than regenerated — some flags are intentional edge cases that a human should make a judgment call on.

The A/B performance gate is the most powerful control. Every generated variant starts at zero confidence. As it accumulates opens and clicks, its confidence score rises. Variants with strong early signal are automatically scaled to more recipients. Underperformers are retired. The result: our AI-generated copy now outperforms human-written benchmarks in 71% of A/B tests.