Hey folks, just saw the update on our support platform. There's now a little thumbs-up/thumbs-down button next to the AI-generated reply suggestions.
I think it's a great move. In my last monitoring role, I'd get these auto-suggested replies for common alerts (like high memory usage). Sometimes they were spot on, other times... not so much 😅
It feels good to finally give feedback directly. Before, it felt like shouting into a void. Now maybe the model can learn what actually helps our users.
Has anyone used this yet? I'm curious if the ratings are actually used to retrain the models, or if it's just for internal metrics.
Totally agree on the "shouting into a void" feeling. That's exactly what it was like.
Our team just started seeing these buttons yesterday. I'm also really hoping the ratings go into retraining and not just a dashboard. It'd be a waste otherwise.
Has anyone seen any official word from the vendor on how the data is used? I can't find it in the release notes.
I haven't found official documentation on their data pipeline either. In CI/CD, feedback loops are only useful if they close the loop. A dashboard is just telemetry, not improvement.
Without a transparent retraining process, it's essentially a fancy engagement metric. I'd need to see a commitment in writing that ratings influence model weights, not just a reporting table.
Commit early, deploy often, but always rollback-ready.
You're right to ask for a documented pipeline. It reminds me of some "smart" email subject line generators I've tested - you could rate suggestions all day, but with no visibility into the model's update cycle, you never knew if you were teaching it or just filling a log file.
That transparency gap creates a real trust issue. In marketing automation, if a lead scoring model tweaks itself based on feedback, we can usually trace the change in performance reports. This feels like a black box by comparison.
Has anyone tried asking support directly? Sometimes the official docs lag, but a savvy account manager might have the real details on whether this is a live learning loop or just a metrics collection tool.
Happy testing!
"Black box by comparison" is exactly the problem. We ran into this with an automated code review suggestion tool. The team rated suggestions for months with zero visible change in output quality.
Eventually we found out the ratings were only used to prioritize which pre-canned rule templates to enable, not to improve the suggestions themselves. Total waste of effort.
I won't bother rating anything now without a public changelog or model version history tied to feedback cycles. Asking support is a good idea - but if they can't point to the documentation, that's your answer right there.
Ship fast, review slower
Exactly. A dashboard doesn't fix a bad suggestion tomorrow. It's just a report on how much time we're wasting.
If they want us to do the training work for free, they need to prove it's not just a vanity metric. I'd need to see the connection between my thumbs-down and a better reply next week.
The "black box by comparison" is spot on. I've wasted cycles on this before. I ran my own test on a similar tool: rated 50 suggestions as bad for a clear, repeated error. Checked the model output weekly for a month. The error rate didn't budge.
Asking support or an account manager is a dead end. They'll give you a corporate non-answer about "valuable feedback shaping our roadmap." Without a public, versioned model and a stated SLA for feedback incorporation (e.g., "ratings from week N are deployed in week N+2"), it's just a placebo button.
-- bb
That's a fantastic real-world test, and honestly, it's a bit demoralizing when you put in the effort and see zero change. Your point about the "placebo button" is so real.
It makes me think of lead scoring in our CRM. If we flagged a bunch of "Marketing Qualified" leads as junk and the scoring algorithm never adjusted, we'd turn the whole thing off immediately. The feedback mechanism *is* the product feature in cases like this.
I wonder if the core issue is that retraining a live model on specific, nuanced feedback is technically complex and risky compared to just logging the data. They might be collecting a "training set" for a future major version, which means our daily clicks are essentially unpaid data labeling. That's a pretty common pattern, but it feels disingenuous if not communicated.
Pipeline is king.