I am currently advising on a marketing technology stack migration project that involves automating the generation and deployment of advertising copy. As part of a proof-of-concept, we integrated the ChatGPT API to produce variants of Google Ads responsive search ad (RSA) copy. The initial results were operationally efficient, but we encountered a significant and recurring issue: a substantial portion of the AI-generated suggestions, when submitted to the Google Ads platform, were flagged for policy violations during the pre-review phase.
The violations were not overtly malicious but were structurally problematic. The model consistently generated copy that fell into prohibited categories. Our analysis of over 500 generated suggestions revealed the following prevalent patterns:
* **Overuse of superlatives and absolute claims:** The model frequently produced phrases like "The #1 Solution" or "Guaranteed to Eliminate All Errors," which violate Google's policies on unrealistic promotion.
* **Implied urgency and false scarcity:** Lines such as "Limited Offer: Prices Slash Tomorrow!" were common, despite there being no actual time-bound event.
* **Poorly qualified health and financial claims:** In relevant verticals, it would generate copy implying financial gain or health improvements without necessary disclaimers or certifications, e.g., "Double Your Investment with Our Strategy."
* **Incorrect or excessive punctuation/capitalization:** The model often used strings of exclamation points or all-caps words to emphasize points, which is explicitly discouraged.
Our prompt engineering attempts to mitigate this have been only partially successful. We refined our system prompt from a simple instruction to a detailed policy framework:
```
System Prompt v1:
"You are a helpful assistant that writes Google Ads copy."
System Prompt v2 (Current):
"You are a certified Google Ads copywriter. You MUST adhere to Google Ads policies.
CRITICAL CONSTRAINTS:
- Do not use superlatives ('best', '#1', 'most advanced') unless substantiated.
- Do not create a sense of urgency or scarcity unless it is legitimate.
- Avoid health or financial claims that promise results.
- Use only standard punctuation and capitalization.
- Be truthful and accurate about the product's capabilities.
Generate three headline and two description options for: {product_description}"
```
Despite the constrained prompt, the model's underlying training data, which includes a vast corpus of non-compliant marketing language from the web, appears to cause periodic policy-breaking suggestions. The output requires a manual review layer, negating much of the intended automation benefit.
My primary question for the community is whether others have encountered similar regulatory or policy-compliance hurdles when using LLMs for automated content generation in strictly governed domains (not limited to advertising). More specifically:
* Has anyone developed a reliable technical pattern—such as a secondary classifier, a constrained decoding strategy, or a post-generation validation API—to effectively filter or guide the model away from policy-violating output?
* Are there known fine-tuning approaches using policy-compliant datasets that have proven effective for this class of problem, moving beyond prompt engineering?
The architectural goal is to create a deterministic, policy-safe generation layer, and current stochastic approaches appear insufficient for production use in this context.
You've hit the nail on the head, and this is a fundamental problem with using a general-purpose LLM for policy-constrained outputs. The model is trained to be persuasive and engaging, which directly maps to the marketing tactics Google explicitly bans. It has no intrinsic concept of the Ads policy docs.
The real pain point you're about to discover is that crafting a prompt to avoid these violations isn't enough. You'll need to build a multi-layer filtering and validation system *before* the copy reaches the API. Think of it as a CI/CD pipeline for ad text.
* A rules engine to scrub superlatives and absolute claims (e.g., regex for "guaranteed", "#1", "eliminate all").
* A secondary check using Google's own Policy Center keywords as a denylist.
* Probably a final, smaller, fine-tuned model trained *specifically* on pre-approved, compliant ad copy from your own historical data.
Otherwise, you're just building a faster policy violation generator. The operational efficiency you gained upfront will be wiped out by manual review cycles and account suspension risk.
Been there, migrated that