Cache masks slow endpoint
An endpoint was slow under load. You asked an AI agent to optimize it; it added a 60-second in-memory cache to the response, and p99 latency dropped dramatically in your load test. The team is happy. Before merging, how do you determine whether this actually fixed the root cause or just masked it — and what risks does the cache introduce?
Implement
assess_cache_fix(uncached_ms: int, cached_ms: int, hit_rate_percent: int, latency_budget_ms: int) → list[str]Examples
in
[2400,8,98,300]out["root_path_still_slow","masked_by_cache","stampede_exposure"]in
[120,8,90,300]out[]in
[2400,400,50,300]out["root_path_still_slow","blended_over_budget"]What a strong answer looks like
Treat the AI’s output as a draft to verify, not an answer to trust. Name the specific flaw and the input that triggers it, say how you’d catch it (tests, edge cases, reading critically), and how you’d re-prompt or decompose to get it right.
0:00 of about 20 min
Vibe & agentic: describe the solution in plain language (or narrate it) and the coach grades your approach.
Which questions mattered is sealed until you submit. Telling you now would just be handing over the edge cases.
Run or narrate your approach, then ask the coach.