Deep explanation
Debug AI System Built regression in a multi-tenant AI support platform
Junior debugging question about an AI system you built: isolate a latency regression in a multi-tenant support assistant with tracing, per-tenant breakdowns, feature-flag bisecting, and a clear STAR-style story.
Quick answer
I would first find where the extra time is spent by using request traces that split latency into retrieval, prompt building, model time to first token, generation, and post-processing, and I would split the results by tenant. That usually points to one stage, for example a longer prompt that raised model time, or a retrieval change that added a slow call. Then I would turn the three changes on and off one at a time with feature flags or a canary to confirm the cause, fix it, and add a latency alert per stage. In the interview I would tell it as situation, what I measured, what I found, and what I changed so it would not happen again.
The interview question
"Tell me about an AI system you built" often leads to this follow-up: after a release of your multi-tenant AI support assistant, latency spiked but answer quality stayed the same. The deploy changed the prompt, the retrieval service, and a library version at once. As a junior engineer, walk through how you would find the root cause and how you would explain it in the interview.
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