Human Review Emerges as the Backbone of AI Evaluation, Powering Smarter Automated Scoring Over Time

Human review is emerging as the backbone of AI evaluation, with teams using domain experts to build golden datasets that power increasingly accurate automated scoring systems over time.

• Human review is being highlighted as a critical component of AI evaluation workflows, enabling teams to build and continuously improve golden datasets by capturing production traces, labeling them with domain expertise, and using that ground truth to tune automated scorers over time.
• A structured review process is emerging as best practice, involving the use of rubrics with pass/fail, categorical, and continuous fields, combined with organized queues that route traces to subject matter experts who fill in clean 'expected' values representing the correct model output.
• As human-reviewed datasets grow, teams are shifting toward scalable automated evaluation by converting recurring human review patterns into heuristic and LLM-as-judge scorers, while avoiding anti-patterns like leaving expected values blank or mixing reference material into ground truth fields.