Rate Limits, Retries, and Idempotency for LLM Clients (ANSWERED)
Scenario question on resilient LLM clients — exponential backoff, 429 handling, idempotency keys, and duplicate-safe writes.
TL;DR — Quick Answer
Wrap LLM calls with client-side rate limiting (token bucket aligned to provider quotas), exponential backoff with jitter on 429/5xx, circuit breakers, and request timeouts. Use idempotency keys for operations with side effects (billing, ticket creation) so retries don't duplicate actions. Distinguish idempotent reads (chat completion retry OK with same seed for eval) vs non-idempotent tool writes. Queue bursty traffic; shard across keys only within provider ToS. Log correlation IDs across retries.
The Interview Question
How do you implement rate limits, retries, and idempotency for LLM API clients in production?
Deep Explanation
Rate limits
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Rate LimitsRetriesIdempotency429ProductionAmazonStripeOpenAI