Your LLM Fallback Needs an Eval Too

1. 1Build a test suite for your LLM fallback model
2. 2Configure retry paths and escalation rules for error handling
3. 3Apply human review to validate fallback model performance
4. 4Test the entire workflow with fallbacks and review
5. 5Compare results with and without fallback evaluation to measure improvement

Data scientists and engineers working with LLMs can benefit from this knowledge to ensure their workflows are robust and reliable

💡 Fallback models and retry paths should be evaluated as part of the overall workflow to ensure reliability

🚨 Don't forget to eval your LLM fallbacks! 🚨