Retry Pattern: The Secret to Resilient Python Code
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This video shows you how to stop your Python code from crashing when APIs, networks, or LLMs fail at random. I walk through the Retry Pattern step-by-step: starting with a flaky example, adding simple retries, improving them with exponential backoff, turning the logic into a clean decorator, and finally adding fallback routes when retrying the same thing no longer makes sense. You’ll also see how retries help when working with LLMs that sometimes return invalid JSON. By the end, you’ll know exactly when to retry, when not to, and how to make your applications far more resilient.
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🔖 Chapters:
0:00 Intro
0:53 The Problem: Transient Failures
1:35 A Simple Retry Function
7:14 Exponential Backoff
9:32 Using a Decorator (with @wraps)
12:53 LLM Example (JSON with the New API)
14:48 When You Shouldn’t Retry the Same Thing
21:06 Production-Ready Option with Tenacity
21:47 When (Not) to Use the Retry Pattern
23:15 Final Thoughts
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