Use case — Engineering, PM
An LLM judge is only useful if it
agrees with your team.
Customers told us their judges were confidently wrong on whole categories of domain-specific failures, and nobody caught it until a human reviewer flagged it. Judge prompts get written once, calibrated against a handful of examples, and shipped. DataFramer builds judges from real reviewer feedback and measures agreement against human scores before you rely on them.
Why judges fail quietly
Generic judge prompts don't know your quality bar.
An off-the-shelf judge has no idea what your reviewers consider acceptable or what a good response looks like in your workflow. It fills in the gaps with general training data.
Agreement with human reviewers is assumed, not measured.
We found that teams write a judge prompt, spot-check a few examples, and ship it. Without measuring agreement against human-scored traces, nobody notices when the judge drifts until something real slips through.
When your quality bar shifts, the judge doesn't.
Teams learn from production and update their standards. Judge prompts are usually static, so calibration goes stale as new failure modes show up.
There's no objective way to compare judge versions.
Without a benchmark, iterating on a judge prompt is guesswork. A version that looks better on a few examples can still perform worse across the full distribution.
How DataFramer builds and calibrates judges
From your team's criteria to a judge you can measure.
Ground the judge in your team's criteria
Judge prompts in DataFramer are built from the rubrics your domain experts already use when reviewing traces. The judge starts from your quality bar, not a generic one.
Rubric Studio
Build a benchmark from traces your team already scored
Select traces with existing human scores and turn them into a judge eval dataset. Each trace must be scored across all selected rubrics to count as ground truth.
Judge datasets
Run the judge and get an agreement number
Run a judge eval against your benchmark. DataFramer compares the judge's scores to the human scores and reports agreement as a percentage per run, per prompt version.
Judge runs
Compare prompt versions against a real benchmark
Every version you test is tracked with its agreement score. You can see which version actually improved and which regressed across the full dataset, not just a sample.
Versioning
Recalibrate when your quality bar changes
When your team revises a rubric based on new failure patterns, run a new judge eval against the updated scores. The benchmark keeps the judge current as your standards evolve.
Recalibration
What you end up with
A judge with a measured agreement score, not a guess.
Agreement score per run
Each eval run shows how closely the judge agreed with human reviewers across the benchmark. A tracked number, not a vibe check.
Prompt version history
Every prompt iteration is stored with its benchmark score. See exactly which version improved, which regressed, and by how much.
Judges grounded in your domain
Built from your rubrics and your reviewed traces. The judge reflects what your team considers good, not what a generic prompt inferred.
Reusable across projects
A judge calibrated for one workflow carries into the next one that shares the same rubric. No starting from scratch.
Regression datasets
The human-scored traces used to calibrate the judge double as regression tests. Fix something, re-run, and see whether agreement moved.
Multi-reviewer support
When multiple reviewers score the same trace, DataFramer aggregates their scores. You see inter-rater agreement across the team before it feeds into judge calibration.
Stop shipping judges on faith.
Free to start. Bring your own model key or use DataFramer credits.