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Almost nobody clicks thumbs-down, and you still need to know when your app is actually failing people.
What you'll have at the end
A weekly count of regenerate and abandon signals sitting next to your near-empty thumbs-down count
You need
An AI app already handling real user traffic, plus a backend that can attach a unique identifier to each reply it generates: a request id, a trace id, anything you can look up again later.
Not covered
Redesigning how the regenerate control itself behaves, capping how many times someone can retry, or feeding any of these counts straight into a training or reward process.
Turn user complaints into regression tests
Where the confirmed misses from the review queue go next: turned into tests that run on every prompt change.
See your LLM's cost, latency, and errors on one dashboard
A natural home for the weekly row once you already trace every reply for cost and latency.
Write your first LLM-as-a-judge grading script
A way to pre-score flagged sessions before a person reads them, once the review queue outgrows manual triage.
Checked 18 Aug 2026
Part of the AI Engineering cookbook