Your LLM Fallback Needs an Eval Too
📰 Medium · LLM
Evaluating LLM fallback models is crucial for reliable workflow performance, and here's how to do it
Action Steps
- Build a test suite for your LLM fallback model using tools like Pytest or Unittest
- Configure retry paths and escalation rules to handle errors and exceptions
- Apply human review to validate the performance of the fallback model
- Compare the results of the fallback model with the primary LLM model to identify areas for improvement
- Run evaluations regularly to ensure the fallback model remains effective and accurate
Who Needs to Know This
Data scientists and engineers working with LLMs can benefit from this knowledge to ensure their workflows are robust and reliable
Key Insight
💡 Fallback models are just as important as primary models and require thorough evaluation to ensure reliable workflow performance
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Don't forget to eval your LLM fallback model!
Key Takeaways
Evaluating LLM fallback models is crucial for reliable workflow performance, and here's how to do it
Full Article
Why fallback models, retry paths, escalation rules, and human review should be tested as part of the workflow. Continue reading on Medium »
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