Fine-tuned models for ad copy at scale: brand voice consistency, per-platform constraints, compliance guardrails, and evaluation against real campaign performance.
Prompted output plateaus fast
Prompt engineering gets you a long way, then stops. The prompt grows to two pages of brand rules and edge cases, output still drifts across a batch, and character limits are violated just often enough that a human reviews every line. At the volume retail media and paid social demand, that review becomes the bottleneck the AI was supposed to remove. Worse, nobody is checking whether generated copy actually performs, so the pipeline optimizes for sounding plausible.
What we build
- Training set construction from your historical creative, weighted toward what actually performed
- A fine-tuned model that holds brand voice consistently across a large batch, not just a good sample
- Per-platform constraint handling: character limits, prohibited claims, and required disclosures enforced in code
- Compliance guardrails for regulated categories, applied as validation rather than trusted to the model
- Generation pipelines that produce structured, testable variants ready for bulk upload
- Evaluation against real campaign performance, so the model is judged on results and not on reading well
How we work
Audit existing creative and performance data to see whether there is enough signal to train on
Baseline a strong prompted model first, because sometimes that is genuinely sufficient
Fine-tune only where the baseline plateaus, then measure both on a held-out set
Ship into the creative workflow with human review, and feed live performance back into training
Typical stack
Frequently asked questions
As a working floor, a few thousand examples of copy with performance data attached. Below that, few-shot prompting with well-chosen examples usually matches or beats a fine-tune. We check this in the audit and will tell you if the honest answer is to improve the prompt instead.
This page is specifically ad copy and creative production at volume: platform constraints, brand voice, and evaluation against campaign performance. The general fine-tuning page covers domain adaptation for other tasks such as classification, extraction, and internal workflows. Same technique, different problem and different evaluation.
It reliably improves consistency and cuts review time, which is the dependable payoff. Performance lift depends on whether your historical data has enough signal to learn from, so we always hold out a test set and measure. We would rather report a flat result honestly than claim a lift the data does not support.
With deterministic validation after generation, never by trusting the model to remember the rules. Prohibited terms, required disclosures, and character limits are checked in code, and anything failing is regenerated or flagged. For regulated categories, human sign-off stays in the loop by design.
Go deeper
Assess your creative data
Tell us how much historical creative and performance data you hold and we will say whether fine-tuning is worth it.
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