AI feedback becomes useful when a rating connects to the task, output version, failure reason and reviewed correction. A thumbs-down count alone does not explain whether retrieval, wording, policy or the underlying source failed. Design feedback collection and adjudication before treating user reactions as training labels.
Let’s plan your AI feedback process.
Bring your workflow and the decisions you need to make. We’ll help you clarify requirements, review the trade-offs and define a practical next step.
Ask what the feedback should change
Separate product usability, answer correctness, source quality and action failure. A user may dislike a correct answer because it communicates an unwelcome rule. Another may approve a fluent answer that contains an error.
Give users simple categories and an optional explanation, with suitable handling for sensitive text. Do not request private documents or credentials merely to make feedback easier to investigate.
Build an adjudication record
| Field | Why it matters | Handling decision |
|---|---|---|
| Task reference | Connects feedback to intended work | Use a permitted internal identifier |
| Output configuration | Makes the case reproducible | Model, prompt and source versions |
| User category | Provides an initial hypothesis | Do not treat it as verified truth |
| Reviewer finding | Establishes the actual issue | Authorised task-aware review |
| Correction | Describes the accepted outcome | Preserve evidence and qualifications |
| Follow-up | Assigns a concrete improvement | Owner, test and release decision |
Restrict access to the supporting content according to its source permissions. Feedback tooling should not become a second unrestricted copy of business data.
Review disagreements explicitly
For a hypothetical policy assistant, one reviewer may mark an answer wrong while another identifies an outdated source. Record the final finding and rationale. The remedy might be a content update rather than prompt tuning.
Where reviewers disagree, use an agreed resolution process. Do not silently average incompatible labels and call the result a reliable dataset.
Close the loop with evaluation
Add permitted, reviewed cases to an evaluation set, keeping an independent holdout where appropriate. Test whether a proposed change fixes the issue without creating regressions in other tasks.
Track correction workload and recurring causes. A reduction in negative ratings is not enough if the product made feedback harder to submit. Keep collection behaviour stable when comparing periods.
Feedback questions
Should every negative rating trigger retraining?
No. Investigate the cause first. Retrieval, source content, interface or operating rules may need a different change.
Can feedback be anonymous?
It can be, depending on the purpose. Define what context is necessary for investigation and avoid collecting identity without a reason.
Use the evaluation dataset guide and production monitoring runbook, then discuss the review process with Drema's AI team.