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Designing your own benchmark for your context
In this lesson
Designing your own benchmark for your context
Design an analytic rubric with 3–5 criteria and descriptive performance-level descriptions at 4–5 levels for scoring model outputs in your production domain, applying principles from validated assessment frameworks…
You'll be able to
- Design an analytic rubric with 3–5 criteria and descriptive performance-level descriptions at 4–5 levels for scoring model outputs in your production domain, applying principles from validated assessment frameworks [^1][^5].
- Construct a 10–20 task benchmark set that represents actual production workflows, selecting criteria that can be evaluated reliably by multiple raters (target inter-rater reliability κ ≥ 0.75) [^7][^8].
- Evaluate model outputs against team-generated ground truth using your custom rubric, distinguishing between holistic scoring (simultaneous consideration of all criteria) and analytic scoring (separate judgment per criterion) based on your use case [^3][^5].
- Classify rubric performance-level descriptions as descriptive (behavioral statements of what the output demonstrates) versus evaluative (rating-scale language such as "excellent" or "poor"), and justify why descriptive language better supports iterative model improvement [^3][^8].
- Apply your benchmark across multiple model iterations or prompt variations, interpreting score changes as evidence of improvement and using rubric feedback to guide refinement, consistent with empirical findings that rubric use predicts higher task achievement [^1][^9].