gen-eval

Automated quality control for AI-generated content applies a predefined rubric and JSON-based scoring to verify output quality before finalization. It checks relevance, specificity, clarity, and safety, automatically retrying generation if thresholds are not met.

Rubric-based scoring validates AI output by evaluating content across four dimensions: relevance, specificity, clarity, and safety. It applies JSON-based scoring against specific criteria and logs pass or retry status to maintain an audit trail.

Automated retry loops can be used in content generation workflows to trigger regeneration when output fails to meet defined quality thresholds. This requires a JSON-compatible output format and a logging mechanism to track pass or retry status and quality scores.

AI content evaluation requires a JSON-compatible output format and a logging mechanism to track evaluation results. This structured format enables the rubric-based scoring system to parse scores and log pass or retry status accurately.

Limitations of automated evaluation loops include strict reliance on a predefined rubric, meaning outputs are only checked against relevance, specificity, clarity, and safety. It requires JSON-compatible formats and cannot assess subjective quality outside these defined criteria.