Your LLM Ground Truth Needs Versioning Too
📰 Medium · LLM
Versioning your LLM ground truth is crucial to maintain trust in extraction evaluations as schemas, rules, and contracts evolve
Action Steps
- Update your LLM pipeline to include versioning for ground truth data
- Implement a change tracking system for schemas, raw-value rules, inference rules, and product contracts
- Configure your evaluation framework to account for version changes in ground truth data
- Test and validate your updated pipeline with versioned ground truth data
- Compare evaluation metrics across different versions of ground truth data to ensure consistency
Who Needs to Know This
Data scientists and engineers working with LLMs benefit from versioning ground truth to ensure reliable evaluation metrics and reproducibility
Key Insight
💡 Versioning ground truth data is essential to maintain trust in LLM evaluation metrics as underlying rules and contracts change
Share This
🚨 Don't let changing schemas and rules compromise your LLM evals! Version your ground truth data for reliable metrics 📈
Key Takeaways
Versioning your LLM ground truth is crucial to maintain trust in extraction evaluations as schemas, rules, and contracts evolve
Full Article
Why extraction evals become hard to trust when schemas, raw-value rules, inference rules, and product contracts change without updating… Continue reading on Medium »
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