I've been using Kling for a few weeks to generate lead scoring logic and email campaign content. It's great, but I kept finding small factual errors, like wrong property names from our CRM schema or date formats that wouldn't parse.
So I built a simple Python script that validates Kling's output against our known data model before anything gets deployed. It just checks for the existence of mentioned fields and correct enum values. Nothing fancy.
The result? The number of times I have to go back and ask Kling for a correction dropped by about 50%. It catches things like "Lead_Status" vs "lead_status" immediately.
Does anyone else do something similar? I'm wondering if there are other common error patterns I should add to the validator, especially for marketing automation workflows.