MediumCodingCoding10 min to answer

Implement a rate limiter for an LLM API.

Asked at

OpenAI

1Give yourself 10 minutes

2Answer out loud, not in your head

3Then compare with the answer below

Stuck? Show a way to structure it+

01Clarify the objective, user, constraints, and acceptable failure modes.
02Structure the response around algorithm, distributed state, burst policy, tests.
03Compare credible alternatives and state the decision criteria.
04Finish with validation, monitoring, and what would change the decision.

Reference answer

strong answer

I would first define the outcome and acceptable error boundary. I would explicitly cover algorithm, distributed state, burst policy, tests. I would select an approach only after comparing quality, latency, cost, safety, and operational complexity, then validate it with representative cases and production monitoring.

algorithm
distributed state
burst policy
tests

Must be adapted to the interviewer’s scenario
Would benefit from concrete numbers and personal examples

alternative answer

A second valid approach is to begin with the highest-risk assumption and test it cheaply. I would explicitly cover algorithm, distributed state, burst policy, tests. I would select an approach only after comparing quality, latency, cost, safety, and operational complexity, then validate it with representative cases and production monitoring.

algorithm
distributed state
burst policy
tests

Must be adapted to the interviewer’s scenario
Would benefit from concrete numbers and personal examples

Grade yourself

algorithm

weight 30%

StrongExplains algorithm concretely and ties it to a decision or measurable outcome.

WeakMentions algorithm without implications, trade-offs, or evidence.

distributed state

weight 25%

StrongExplains distributed state concretely and ties it to a decision or measurable outcome.

WeakMentions distributed state without implications, trade-offs, or evidence.

burst policy

weight 25%

StrongExplains burst policy concretely and ties it to a decision or measurable outcome.

WeakMentions burst policy without implications, trade-offs, or evidence.

tests

weight 20%

StrongExplains tests concretely and ties it to a decision or measurable outcome.

WeakMentions tests without implications, trade-offs, or evidence.

Then expect these follow-ups

What are the time and space complexities?
Tests: depth
Which edge cases would you test?
Tests: adaptability
How would this work across multiple processes?
Tests: adaptability

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