Effective AI evaluation is about more than finding factual errors. It requires sound editorial judgment.
A response may be accurate but fail the user's instructions. It may be well written but poorly reasoned. It may omit important context, make unsupported assumptions, or express unwarranted confidence.
Drawing on more than 30 years of editorial experience, I evaluate AI-generated content for:
Accuracy
Instruction-following
Reasoning
Completeness
Audience fit
Editorial quality
The case studies below examine real-world AI outputs, explain the reasoning behind each evaluation, and demonstrate practical techniques for improving model performance through careful human review.
Can an AI-generated response be factually accurate yet still fail the user's objective?
What distinguishes editorial evaluation from simple proofreading?
What separates an effective AI prompt from one that produces inconsistent or unpredictable results?
Should an AI-generated response be judged by how confidently it is written?
Why do different AI models produce different responses to the same prompt?