The Fallback That Never Fires

1. 1Identify primary model rate limiting issues
2. 2Implement fallback logic to detect rate_limit_error
3. 3Select and retry with alternative models in the fallback chain
4. 4Monitor and adjust fallback logic for optimal performance

AI engineers and developers benefit from understanding fallback logic to prevent rate limiting issues, ensuring seamless model switching and minimizing errors

💡 Fallback logic may not always work as intended, leading to repeated rate limiting issues

🚨 Fallback logic not working as expected? Check your model rate limiting setup!