Model evaluation & grading
Human model evaluation, scored against your rubric
When you need a number you can track across releases, you need expert graders and a rubric they apply the same way. Pathwize scores your model outputs against your standard and shows you how trustworthy the score is.
Talk to us about model evaluation & grading
Where the usual approach falls short
Vague rubrics, unreliable numbers
A loose instruction produces as many private definitions of quality as you have graders. The headline number then means nothing you can defend.
No visibility into grader agreement
Most evaluations report a score with no measure of how much graders agreed, so you cannot tell a real result from noise.
Crowd graders on expert content
Reasoning, clinical, legal, and security outputs need graders qualified to judge them, not a crowd applying a rubric they do not understand.
What you get
Rubric-driven scoring
We grade against your rubric, pass/fail or on a scale, with each level defined so two experts land in the same place.
Agreement you can see
Items are double-graded where it matters and inter-rater agreement is reported per batch, so you know the score is trustworthy.
Qualified graders
Outputs are scored by credential-verified experts in the relevant domain, not a general crowd.
Reproducible provenance
Every score carries a signed record of who graded what against which rubric, ready for audit.
How the engagement runs
Bring your rubric
Share the rubric and pass bar, or we help you turn a fuzzy quality sense into a gradable standard.
Matched expert graders
Credential-verified graders in your domain are assembled and calibrated on your rubric.
Graded with agreement checks
Outputs are scored in a sandboxed workspace with double-grading and live agreement tracking.
Benchmark you can defend
Receive scores, agreement metrics, and a provenance bundle you can reproduce.
What you receive
- Per-output scores against your rubric (pass/fail or scale)
- Inter-rater agreement per batch
- Signed provenance for every grade
- A benchmark you can re-run and defend release over release
FAQ
Model evaluation & grading, answered
An expert scores a single model output against a rubric, either pass/fail or on a scale. It produces a measurement you can track, unlike a preference task, which compares two outputs.
Graders are calibrated on your rubric, items are double-graded where it matters, and inter-rater agreement is reported so you can see whether the rubric is being applied consistently.
Yes. If you have a quality sense but no formal rubric, we help turn it into a gradable standard with defined levels and edge-case handling.
Verifiable model evaluation & grading, on your data
Source expert data with provenance built in, EU-native and audit-ready. Book a demo with your ML and compliance teams.