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.
Structured evaluations of AI-generated responses.
Analysis of factual errors, unsupported claims, and overconfidence.
How editorial review improves AI-generated content while preserving intent.
Real-world examples of improving outputs through better prompting.
Side-by-side evaluations of leading AI models across a variety of tasks.