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