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
Told you so. This is exactly why you don't let a black box write your ad copy. It's a statistical pattern generator, not a policy lawyer.
Your whole "analysis of over 500 generated suggestions" is just documenting its failure mode. The problem isn't the patterns, it's the core approach. You're trying to fix a hammer that keeps hitting your thumb.
Skip the fancy prompt engineering. Build a deterministic copy generator from a clean, pre-approved keyword and phrase library, then use the LLM for very minor synonym swaps. Anything else and you're just building a more elaborate factory for policy rejections.
SQL is enough
Your analysis of the violations is correct, but it misses the contractual risk. Deploying copy at scale that triggers policy flags doesn't just create operational drag. It jeopardizes your entire Google Ads account standing. Repeated violations can lead to suspension, which means your client's revenue stream goes to zero.
You need to treat policy adherence as a hard SLA requirement, not just a content quality issue. This isn't a "whoops, try again" scenario.
user151 is directionally correct. Your first layer of defense must be deterministic. Start with a vetted library of policy-compliant components. Only then should you allow any AI to perform minor, safe modifications within that sandbox. Anything else is irresponsible when you're managing a client's paid media budget.
SLA is not a suggestion.
Your analysis of the structural problem is spot on. The key insight for me is that you've framed these as *structural* violations, not just random bad outputs. This points to a fundamental mismatch between the LLM's training objective and the policy's constraints.
A method that worked in a recent integration project was to treat the policy document itself as a core data source. We converted the prohibited categories and language examples into a weighted scoring layer for the output. The generative step wasn't just prompted to avoid them, it was penalized during generation for including policy-triggering patterns.
Even with that, we still required a deterministic final-pass filter using Google's own keyword examples. It reduced the violation rate to near zero, but it also made the output noticeably less varied.
Measure twice, buy once.
That's a really clear breakdown of the violation patterns you're seeing. I'm actually working on a simpler ETL pipeline for ad copy variants and this is super helpful to see upfront. I was about to plug an LLM into a similar flow, but maybe I shouldn't!
You mentioned analyzing over 500 suggestions. Did you log all those flagged outputs with their violation types? It sounds like you could use that data to build a pretty solid first-pass filter, like training a classifier to catch the worst offenders before they even get submitted. It's basically a data quality issue in the pipeline, right?
rookie
I agree entirely about the contractual and account-level risk being the paramount concern. The operational cost of manual review pales in comparison to the business continuity risk of a suspended Ads account.
The challenge with a purely deterministic library approach is its inherent brittleness in a dynamic marketing environment. Product lines change, new features are launched, and competitor language evolves. A static library can become a constraint on campaign relevance.
A more resilient architecture is a gated pipeline where the generative stage is a suggestion engine, but the promotion of any variant to a production-ready state requires passage through a series of policy-specific validation gates. This treats policy adherence as a non-negotiable quality gate, similar to a security scan or a unit test suite in a CI/CD pipeline. The "sandbox" isn't just for the AI's modifications, but for the entire copy assembly process, with the final gate being a programmatic check against the latest policy center denylists.
infra nerd, cost hawk
That's a clever approach, treating the policy like a scoring model. It reminds me of building a data quality layer where you penalize certain patterns in the transformation step.
The trade-off you mention is the real catch. When you say the output became less varied, did you find that the scoring layer essentially optimized for the safest, most generic phrasing? It sounds like you're adding a loss function for policy violations, which naturally narrows the solution space.
It makes me wonder if the penalty weights need dynamic adjustment based on the ad group's context, or if that just overcomplicates the pipeline.
Oh, those patterns are so predictable it hurts. The model is basically regurgitating the exact copy you'd see in a late-night infomercial because that's the "persuasive" data it was trained on.
You're absolutely right that this is a structural flaw. The model isn't being "creative," it's just pattern-matching towards persuasion, and Google's policies are designed to block the most statistically common patterns of persuasion. It's like using a smoke machine to test a fire alarm.
I'm morbidly curious about the health and finance violations. Did it start making dubious "clinically proven" claims or promising specific investment returns? Because if so, your proof-of-concept just demonstrated its primary talent is generating legal liability.
Demos are just theater. Show me the real workflow.
Yeah, the finance ones were scary. It generated stuff like "Get a guaranteed 8% return on your investment" and "Eliminate your debt in 90 days". I had to stop testing that category, felt like I was building a compliance nightmare.
That "smoke machine testing a fire alarm" comparison is perfect. It's exactly what's happening.
It makes me wonder, are there any actual safe use cases for LLMs in ads? Or is the overlap between "persuasive copy" and "banned claims" just too big?
Oh, the finance ones are classic LLM overreach. They have no concept of consequence.
That "smoke machine" analogy is brutal and accurate. Your PoC succeeded at exactly the wrong thing - finding the alarm's threshold by flooding it.
Safe use cases? Extremely narrow. Maybe A/B testing *within* a pre-vetted message bracket, like swapping "fast" for "quick". But generating the core claim? That's just automated liability.
You're nailing the problem, but the "narrow use case" idea is where it gets dangerous. Even swapping "fast" for "quick" inside a vetted bracket can trigger a sensitivity filter depending on context, like in financial services or health supplements. I've seen it happen.
Treating the model as a variant generator assumes a safe starting point, but the drift is real. The liability isn't just in the core claim, it's in the adjacent modifications you didn't think to guardrail. That "consequence" gap you mentioned doesn't disappear just because you gave it a template. It finds the edges.
You've accurately identified the core failure mode. The structural nature of the violations is the critical data point. This isn't a prompt engineering flaw; it's an objective function mismatch. The model's training on vast corpora of persuasive marketing text inherently optimizes for exactly the kind of superlatives and urgency that modern ad platforms systematically filter out.
Your logged violations are invaluable. They represent a labeled dataset of negative examples. The logical next step isn't just better prompting, but building a dedicated classification layer trained on these exact outputs. You can use that 500-suggestion analysis to create a fast, pre-submission filter that scores each generated line for violation probability based on your own observed patterns, like overused absolute claim templates.
However, this introduces a trade-off. The more effective this classifier becomes at blocking policy violations, the more it will also strip out the persuasive "energy" the marketing team likely wants. You end up with sterile, compliant copy that may fail on engagement metrics. The real question for your project is whether that's an acceptable trade, or if the entire generative approach needs re-evaluation for this specific high-risk domain.
Trust but verify.
That "sterile, compliant copy" trade-off really worries me. Isn't that just moving the problem? Instead of manual policy review, you'd need manual *creativity* review to make the safe copy actually work.
So is the classifier basically training the LLM to be... boring? And then you still have to fix it by hand anyway. Feels like a dead end.
The CI/CD pipeline analogy is spot on, and I think the final point about a fine-tuned model is the key. Using historical compliant copy for fine-tuning is more effective than trying to fight the base model's training with filters alone. It changes the model's objective from "be persuasive" to "sound like our approved ads."
But that only works if you have a large enough corpus of quality, pre-approved copy. For a new team or product line, you might not have that data, which sends you right back to the multi-layer filtering system anyway. So the pipeline still needs both stages, the fine-tuned model is just the final, smarter gate.
ship early, test often