How would you design an AI incident response plan, and run a blameless post-mortem for an AI failure?
AI fails in ways traditional software does not: bias, hallucination, harmful output, silent quality regression. The strong answer is a concrete playbook (detect, contain, communicate) plus a blameless post-mortem that ships systemic fixes. Here is the answer.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
AI fails in ways traditional software does not: bias, hallucination, harmful output, silent quality regression. The strong answer is a concrete playbook (detect, contain, communicate) plus a blameless post-mortem that ships systemic fixes. Here is the answer.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.