LLM Application Development
Intermediate
4.5

Handle Rate Limits and Retries

Make your LLM calls resilient in production.

0h 20m
1 lesson
1.2K students

What You'll Learn

Learning objectives will be added soon.

Tutorial Content

Failures are normal

APIs return rate-limit (429) and transient errors. Production code must retry gracefully instead of crashing.

Exponential backoff

import time, random
def with_retries(fn, tries=5):
    for i in range(tries):
        try:
            return fn()
        except RateLimitError:
            time.sleep((2 ** i) + random.random())  # backoff + jitter
    raise RuntimeError("exhausted retries")

Also worth doing

Set timeouts, cap concurrency, and respect any Retry-After header. The jitter prevents many clients from retrying in lockstep and hammering the API at once.

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Tags

API
LLM
Python