Preference ranking
Pairwise and n-way response comparison against a written rubric, with the tie and near-tie handling defined rather than left to the rater.
LLM Data Labeling & Model Evaluation
Human judgement, applied consistently
What this covers
LLM evaluation work differs from annotation in one important way: there is often no ground truth. Which of two responses is better is a judgement, and the value of the data depends entirely on whether that judgement was made against a consistent rubric.
This is where agreement rates matter most and where they are naturally lowest. A rubric that has not been stress-tested produces raters who each apply a private standard, and preference data built that way teaches a model the average of several disagreements.
StaffCloud runs this work rubric-first: the criteria written and worked through on examples, raters calibrated against them, agreement measured, and disagreement cases adjudicated rather than averaged away.
What the team does
Prompt and response review
Responses assessed for accuracy, helpfulness, instruction-following and tone against your criteria, scored rather than described.
Red teaming and adversarial review
Structured probing for the failure modes you are worried about, documented reproducibly so a fix can be verified.
Scored model evaluation
Model versions evaluated on a fixed rubric and a held task set, so comparisons between versions mean something.
Rater calibration
Raters calibrated on worked examples before production, with agreement measured and drift re-checked during the project.
Rubric development
The rubric built and revised with you — the part that determines whether the resulting data is signal or noise.
How the engagement runs
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01
Rubric development
Criteria written with worked examples, including the cases you expect to be contentious. This is most of the value of the engagement.
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02
Calibration
Raters worked through the examples, agreement measured, and the rubric revised where raters reasonably disagree.
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03
Production
Rating at volume with a defined adjudication path for disagreement, rather than averaging it away.
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04
Report
Results with agreement statistics and the disagreement cases surfaced, because those are often the most informative output.
Why the rubric is the deliverable
The ratings are downstream of it. A weak rubric produces confident data that means nothing.
- Criteria that have been argued over before they were applied
- Agreement measured, with disagreement adjudicated not averaged
- Evaluations comparable between model versions
Platforms and tools we work in
The pod works inside your stack. These are the systems this team uses day to day — if yours is not listed, we will learn it.
- Label StudioRating and comparison interfaces
- Your evaluation harnessWe work in yours where you have one
- PythonAgreement scoring and analysis
- Excel / Google SheetsRubric versions and adjudication logs
- Secure environmentsWhere prompts or outputs are sensitive
Questions buyers ask
The things worth knowing before the first call.
Not sure this is the right scope?
Describe the workload in a sentence and we will point you at the right team — or tell you honestly if it is not something we should take on.
Ask usLower than for object detection, and that is expected rather than a problem. Preference judgements have genuinely lower natural agreement between people.
StaffCloud reports the real figure measured on your task. A vendor quoting a high agreement rate for subjective work is telling you one of two things: the rubric is too trivial to be useful, or the number is not real. Knowing the true figure is what lets you judge how much weight the results carry.
StaffCloud runs structured adversarial review against failure modes you define, documented so that findings can be reproduced and re-tested later.
What that produces is evidence for your own safety assessment, not a verdict on the model. We are not in a position to make claims about a model's overall safety and would not offer to — the value is in the documented findings your own team can act on.
For domain tasks, yes, and it shapes both the cost and how quickly the team can scale.
It is worth being explicit about the level a task genuinely needs. Over-specifying is expensive and slows recruitment; under-specifying is worse, because the ratings look complete while missing the distinctions that mattered. StaffCloud works that out with you during scoping.
A senior reviewer adjudicates, and where a case type keeps recurring the rubric is changed rather than the disagreement re-litigated each time.
Averaging disagreement is the alternative, and it is a poor one: the most contentious cases — usually the most informative ones — get flattened into meaningless middle values. Adjudicating and then fixing the rubric turns the disagreement into something useful.
Yes, on a fixed rubric and a held task set, so both models are judged the same way.
The comparison is only as good as how representative that task set is of real use, which makes it something to design deliberately rather than assemble from whatever is to hand. StaffCloud works through that with you before running the evaluation.
Yes — the ratings, the rubric and the adjudication record all belong to you.
The rubric is the piece worth holding on to. It is what makes a later evaluation comparable with this one, so you can tell whether a model has actually improved rather than whether the standard moved.