Ground truth curation and metric interpretation best practices for evaluating generative AI question answering using FMEval

This article discusses best practices for curating ground truth data and interpreting evaluation metrics when using FMEval to evaluate question answering generative AI pipelines.

Specifically, the article covers:

• Understanding the Factual Knowledge and QA Accuracy metrics in FMEval
• Best practices for curating ground truth data for Factual Knowledge and QA Accuracy
• Interpreting Factual Knowledge scores to detect hallucinations and retrieval issues
• Interpreting QA Accuracy metrics like recall, precision, and F1 to assess closeness and conciseness to ground truth
• Using ground truth curation and metric interpretation in an iterative flywheel process to improve the golden dataset and evaluation process
• Key takeaways on balancing ground truth curation with metric interpretation for effective evaluation