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The short answer
An AI error report becomes actionable when the team can identify the intended task, reconstruct the relevant configuration and evidence, state the expected behavior, and verify a correction. Preserve the user’s report separately from the investigated finding. An unhappy outcome is a useful signal, but it does not by itself identify the failing component.
What to take away
- Ask what the user needed and what actually happened.
- Collect the minimum evidence permitted for reproduction.
- Keep a corrected regression case and check the released behavior.
Sources checked . Research synthesis and Rubrex recommendations. Examples and local experiments are labeled; they are not client results.
Capture an outcome description rather than a guess at the cause
Google’s SRE book describes postmortems as records of an incident, impact, response, contributing causes, and follow-up actions. Rubrex applies that learning-oriented structure to AI error intake, while keeping a small user report distinct from the fuller investigation that may follow.
Imagine a fictional user reporting that a document assistant quoted an outdated return deadline. Ask for the task, observed answer, and relevant time or request identifier through an approved reporting route. Do not require the user to paste an entire confidential conversation or decide whether retrieval, the prompt, or the model caused the mistake before the team will accept the report.
Evidence: Site Reliability Engineering: Postmortem Culture [1]
Reconstruct only the evidence the investigation needs
Find the application version, prompt and model settings, retrieved document identities, and available outcome records for the reported task. Check access before retrieving them. If raw content was not retained, say what is missing and use a sanitized reconstruction where appropriate rather than fabricating the exact original request.
For the deadline example, compare the current policy with the passage actually supplied to the assistant. An archived document, a stale index, a lost qualifier, and unsupported generation imply different fixes. Keep observed evidence separate from hypotheses in the investigation note so a plausible explanation is not mistaken for a reproduced cause.
Write the smallest case that preserves the failure
Create a case with the necessary question, source snapshot, allowed behavior, and expected result. Retain a link to the protected incident record rather than copying private details into a broadly shared test suite. If the defect depends on timing or permissions, preserve those conditions in the case instead of reducing it to a generic question.
Run the candidate correction against the reproduced failure and legitimate neighboring cases. In the fictional deadline scenario, changing every answer to use the newest date could break historical-policy questions. Include one question about the old period and one unsupported request. A narrow fix should resolve the observed failure without erasing distinctions the application is expected to maintain.
| Case | Evidence to keep | Decision |
|---|---|---|
| Reported deadline error | User task and available request reference | Accept the signal without assuming a cause |
| Reproduced stale passage | Document and index versions | Choose the component-specific correction |
| Historical-policy control | Question period and accepted source | Preserve legitimate past-period answers |
Close the report only after the correction reaches its intended boundary
Record whether the issue was reproduced, which component changed, what evidence supports the fix, and which limitations remain. Verify the released configuration or index when local and production behavior can differ. A passing local regression is a useful milestone; it is not proof that every serving worker adopted the correction.
Use repeated reports to improve detection and documentation, but do not treat complaint counts as unbiased failure prevalence. Some users report more readily than others and some errors are difficult to notice. Keep user reports alongside representative evaluation and monitoring so the team learns from incidents without claiming the inbox describes the whole workload.
Limits of the evidence
This is a proposed intake-to-regression workflow with a fictional deadline error. Available telemetry, user permissions, and retention choices constrain reproduction. No real user incident, executed fix, or measured reduction in errors is reported.
Common questions
What if the exact request cannot be recovered?
Label the gap and distinguish a sanitized reconstruction from an exact reproduction. Investigate what the available evidence can support.
Does fixing the reported case close the issue?
Also test adjacent legitimate cases and verify the intended released boundary before claiming the correction is deployed.
Sources & further reading
Primary sources behind this briefing. A source’s findings apply to its own study conditions; publication on arXiv does not establish peer review.
- Site Reliability Engineering: Postmortem Culture Google SRE · Living reference · Foundational book referenceReviewed: foundational incident impact, response, contributing causes and follow-up records; proposed AI adaptation is original. Accessed October 16, 2026.
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