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The core idea
A rubric separates the dimensions of quality that matter for the task. Each dimension needs observable anchors, not just a number from one to five. Keep the rubric small enough to apply consistently and include examples that illustrate difficult boundaries. Different errors may have different consequences, so avoid hiding critical failures inside an average.
A practical approach
Step 1
Choose dimensions such as factual support, completeness and usability. Explain what each dimension includes and excludes.
Step 2
Write anchors for strong, partial and failed performance using concrete output characteristics.
Step 3
Have two reviewers score the same sample independently, then discuss disagreements and revise unclear anchors.
Worked example
For a workshop summary, completeness might require all agreed action items, while factual support checks whether owners and dates were actually stated. A concise summary can score well on readability and still fail if it invents a deadline.
A mistake to avoid
A single ‘overall quality’ score makes diagnosis difficult. Too many dimensions create reviewer fatigue and inconsistent scoring. Keep only the distinctions that change your decision.
Try it yourself
Create a three-dimension rubric for one AI task and score five outputs. Rewrite any anchor that reviewers interpret differently.
Your implementation checklist
- Dimensions reflect the task’s real risks.
- Score anchors describe observable behaviour.
- Reviewers compare disagreements.
- Critical failures are visible separately.
Share or cite this guide
Innovate with Ajay Editorial (2026-10-04). Write an Evaluation Rubric with Clear Anchors. https://innovatewithajay.com/blogs/write-an-evaluation-rubric-with-clear-anchors
Editorial approach, sources and corrections
Apply this guide to your own work: build a project brief with inputs, test cases and review boundaries.