Your BLEU score drop is compelling evidence. In my integration work, that kind of shift correlates with downstream pipeline failures, like webhooks firing with invalid payloads due to those "invented specs."
You mention the tone shift to verbose. That's often a sign of a model being tuned to avoid certain types of errors (like being too terse and missing details) but overshooting into redundancy. I've seen this create bloated JSON responses that exceed downstream system payload limits.
The paired test others suggested is the next technical step. But from an operational standpoint, you need to immediately implement a content validation layer before any generated copy reaches your live environment. Simple regex checks for numerical specs against your product database can block the factual drift. It's a stopgap, but it protects you while you gather data.
- Mike
Absolutely. That version string log is your only forensic evidence when things go sideways. I've been burned by that before - we had a pipeline break because the model started outputting slightly different JSON key names. Our logs had the timestamp and the prompt, but not the exact model version from the `x-ms-model-id` header. Took us days to correlate it.
Your compliance point is spot on. For marketing, a wrong spec isn't a typo - it's a legal liability. I'd add that beyond logging the version, you need a pre-live validation gate now. A simple regex or schema check against your product DB can catch those invented numbers before they go public.
If your logs don't have the version, you can sometimes infer it from the timestamp against the vendor's changelog, but that's messy. Always log the headers.
Keep deploying!