Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval

This article provides comprehensive guidance on generating and reviewing ground truth for evaluating generative AI question-answering applications using FMEval, with a focus on best practices for scaling ground truth generation.

• Ground truth generation involves creating high-quality question-answer-fact triplets using large language models
• A serverless batch pipeline architecture is proposed for automating ground truth generation at enterprise scale
• Two key approaches for judging ground truth quality are discussed:
  • Human-in-the-loop review to detect hallucinations and verify information accuracy
  • LLM-as-a-judge for automated review and remediation
• Risk assessment is crucial in determining the level of ground truth review required
• The process aims to develop responsible AI solutions with high-quality evaluation datasets

The article emphasizes the importance of creating deterministic evaluation processes for generative AI question-answering assistants while maintaining accuracy and reliability.