AI Response Evaluation Practice
Practiced evaluating AI-generated responses for clarity, quality, and factual accuracy in medical education contexts. This involved reviewing outputs, identifying issues, and refining prompts to improve response quality. The work aligns with AI response evaluation and rubric-based checking behaviors often used in AI training and quality assurance. • Compared AI-generated outputs for quality and accuracy • Checked clarity, quality, and factual accuracy of responses • Used rubric-based evaluation concepts (quality criteria) • Performed iterative improvements to prompts and outputs