Deep explanation
Debug OpenAI regression in a multi-tenant AI support platform
Junior debugging question: find why OpenAI-powered replies got slower using per-stage timing, response headers, token usage, rate limits, and prompt caching behavior in a multi-tenant app.
Quick answer
I would first time each step of a reply, so I know if the slow part is our code or the OpenAI call. For the OpenAI call I check token usage per request, 429 rate-limit errors and retries, and whether the new prompt template broke prompt caching by putting changing text at the start. I'd also group latency by tenant to see if the new large customer is eating our shared rate limit, then fix the specific cause and confirm with the same metrics.
The interview question
After a release, your multi-tenant support platform's AI replies got slower (p95 latency jumped) but answer quality looks the same. The app calls the OpenAI API, and the release changed the prompt template, upgraded the OpenAI SDK, and added a new large customer. How would you find the cause and fix it?
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