Production-Grade Error Handling: Timeouts, Rate Limits, and Retries

Every AI application hits these three errors in production. If you aren't prepared for them, your app will crash, and your users will leave. In today's video, we break down the engineering patterns for handling unreliable LLM providers.

The Production Checklist:

Network Timeouts: These happen when the provider is slow or under heavy load. The fix? Catch the specific timeout error and implement exponential backoff to retry the request.

Rate Limit Errors (429): This means you've sent too many requests too fast. Don't just crash; catch the 429 status and wait for the "cool down" period before retrying.

Specific Exception Handling: This is the most important rule. Never catch a bare Exception. Always catch the specific error type (e.g., openai.RateLimitError) so you know exactly what went wrong.

Why this matters:
Handling errors properly is what separates a "toy project" from a production-grade system. By catching specific errors, you can build a resilient pipeline that stays up even when the API provider is having a bad day.

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Видео Production-Grade Error Handling: Timeouts, Rate Limits, and Retries канала learnwithparam - AI Engineering Society