Tell me about a production failure you owned.
- 1 min — what broke and the blast radius
- 1 min — detection and mitigation timeline
- 2 min — root cause, including your own decision
- 1 min — systemic fix, results, regret
The interviewer wants accountability without self-flagellation: what you broke, how fast it was detected, how you mitigated, and the systemic fix — ideally one that improved detection, not just the specific bug.
AI-specific failures make strong stories: a prompt change that broke grounding, a cache that leaked across tenants, a model deprecation nobody tracked.
At Staff, ownership extends to the process that allowed the failure. The postmortem is blameless, but you name your own decision in it.
What the answer sounds like at each level
Owned a bug they shipped, fixed it quickly, added a test.
Owned mitigation, root cause and a detection gap; added a gate and an alert used by others.
Owned the process failure, changed release practice for prompts across teams, and was candid about their earlier opposition to the control.
Follow-ups the interviewer will push
1Why did the eval pass?
It scored fluency and correctness with an LLM judge but never checked whether citations were present. We measured what we'd thought to measure — the fix was a deterministic check, not a better judge.
2How did you communicate it?
A short note to affected users within the day, the postmortem shared openly, and a 15-minute walkthrough at the eng all-hands so other teams added the same gate.
Red flags interviewers listen for
- The failure was someone else's and the candidate 'helped'.
- Fix is only the specific bug; no detection improvement.
- Defensive or vague about their own role.
How it's scored
| Dimension | What strong looks like |
|---|---|
| Accountability | Names own decision as a contributing cause, without drama. |
| Timeline | Detection, mitigation and fix times with numbers. |
| Systemic fix | A gate and alert that later caught other teams' changes. |
A prompt change that dropped citations
Illustrative exampleI shipped a prompt revision to our internal-wiki assistant used by ~9,000 employees. It improved answer fluency on our eval set but, as we found later, removed the instruction ordering that kept the model citing sources. Uncited answers went from 3% to 41% of responses.
I was the author and the on-call engineer that week. I needed to mitigate, find the cause, and close the gap that let it through.
A support lead flagged it 26 hours after deploy from user complaints. I rolled back via the prompt flag within 15 minutes of the report, confirmed citation rate recovered on live traffic, then diffed outputs on 200 sampled queries. Our eval measured answer quality but had no citation-presence check — that was the real gap, and I had written that eval. I added a citation-rate metric to the eval gate (fail below 95%), a live dashboard with an alert at 10% uncited over an hour, and made prompt changes go through a 5% canary. I wrote the postmortem and named my missing check as the contributing cause.
Re-shipped the improved prompt with a fixed ordering two days later: fluency gains kept, uncited rate 2.5%. In the following six months the citation gate blocked two other prompt changes from other teams. My regret: 26 hours of detection time. I'd have caught it in one hour if the canary had existed, and I'd argued against canaries for prompts months earlier as too heavy.