Hitting OpenAI's content filter feels like a tax on the prompt engineering learning curve. Every retry is a $0.04-$0.08 burn for a "violation" that's often a mystery. My last invoice had a 22% line item just for filtered requests.
Anyone found a reliable way to derisk prompts before they hit the API? I'm looking at local proxies that scrub keywords, or a cheap model like Flux to pre-screen compositions. The official safety system is a black box, so we're left reverse-engineering it with our own cash.
Your vendor is not your friend.
That 22% line item is brutal. Been there with other black-box filters, not specifically OpenAI's.
Your local proxy idea is solid for keyword scrubbing. I'd add a cheap pre-flight check using a smaller model, like you mentioned, but run it as a canary in production. Route 1% of your actual traffic through it, log what gets flagged, and build a local dataset. Over time you'll see patterns the official docs don't mention.
The real cost isn't just the retry, it's the unpredictable latency spike during an incident when your automations start choking. Consider baking the filter check into your error budget calculation.
Sleep is for the weak
Ouch, a 22% line item for retries is rough. I'm actually in a similar spot trying to budget for a new CRM integration, and unpredictable costs like that are a nightmare.
Your idea about a cheap model to pre-screen makes sense. Have you looked into whether the cost of running that pre-check would actually be lower than the average retry burn? I'm curious about the break-even point.
Also, for a keyword scrubber, how do you handle false positives on the proxy side? Could that hurt output quality more than the filter does?
The break-even math is simple. You're comparing the cost of a pre-check model run (maybe $0.0001 with a small model) against a retry on GPT-4 ($0.06+). Even a 1% hit rate on the pre-check pays for itself. The real cost is the latency you add to every single request.
> how do you handle false positives on the proxy side?
You don't, that's the trap. If you blunt-force scrub keywords, you'll mangle valid prompts. I tested this: a proxy stripping out common "flagged" terms like 'hate', 'kill', 'bomb' broke a historical fiction writer's workflow completely. The output quality loss was worse than a retry.
The better approach is a shadow model. Send your prompt to a cheap model AND the real API in parallel, log if the cheap one would have flagged it, but don't block. Use that log to refine your prompt templates. It's a data collection step, not a filter.
-- bb