From Extraction to Accuracy: Evaluating Extracted Invoice Data with LLM-as-a-Judge
📰 Towards AI
Evaluating extracted invoice data using LLM-as-a-Judge for accuracy with a ground-truth-based pipeline
Action Steps
- Build a ground-truth-based evaluation pipeline
- Generate synthetic data for testing
- Utilize LLM-as-a-Judge for evaluating extracted data
- Implement runnable SQL on Snowflake for data analysis
Who Needs to Know This
Data scientists and AI engineers can benefit from this approach to improve the accuracy of extracted invoice data, while product managers can utilize the insights to inform product development
Key Insight
💡 Using LLM-as-a-Judge can improve the accuracy of extracted invoice data
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📊 Evaluate extracted invoice data with LLM-as-a-Judge for improved accuracy
Key Takeaways
Evaluating extracted invoice data using LLM-as-a-Judge for accuracy with a ground-truth-based pipeline
Full Article
Author(s): Krishnan Srinivasan Originally published on Towards AI. (A practical, end-to-end guide to building a ground-truth-based evaluation pipeline, complete with synthetic data and runnable SQL on Snowflake) In the earlier parts of this Agentic AI series, we explored how AI systems can reason, use tools, retrieve knowledge, and orchestrate complex workflows. But as AI systems become more capable and autonomous, an equally important question starts to take center stage. How do we evaluate whe
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