Consistent across a batch
Brand voice holds across thousands of variants, not just a good sample.
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Fine-tuning for ad copy
LLM fine-tuning for ad copy and creative at scale.
Generic models write generic ad copy: fluent, on-brief and completely off-brand. When you need thousands of variants a month that sound like the brand and respect platform rules, fine-tuning on your own performing creative is what closes the gap.
- Brand voice
- Platform constraints
- Performance-evaluated
What you get
Copy that sounds like the brand, at volume.
Rules enforced in code
Character limits, prohibited claims and disclosures validated.
Less line-by-line review
Review time drops when output stops drifting.
Judged on results
Evaluated against real campaign performance.
The problem
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
What's included.
- 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
How we run it.
Step 1
Audit existing creative and performance data to see whether there is enough signal to train on
Step 2
Baseline a strong prompted model first, because sometimes that is genuinely sufficient
Step 3
Fine-tune only where the baseline plateaus, then measure both on a held-out set
Step 4
Ship into the creative workflow with human review, and feed live performance back into training
What we use
Tools, platforms and methods.
- OpenAI fine-tuning
- Amazon Bedrock
- LoRA / PEFT
- PyTorch
- Python
- Snowflake
FAQ
Common questions.
How much creative history do we need to fine-tune?
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.
Is this general-purpose fine-tuning?
No. This work is specifically ad copy and creative production at volume: platform constraints, brand voice and evaluation against campaign performance. Fine-tuning for other tasks, such as classification or extraction, uses the same technique against a different evaluation.
Will fine-tuned copy actually perform better?
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.
How do you handle regulated claims and compliance?
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.
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Learn moreAssess your creative data.
Tell us how much historical creative and performance data you hold and we will say whether fine-tuning is worth it.