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A reliable LLM-judge rubric states exactly what is being judged, what evidence counts, and what separates a pass from a fail. Break broad quality into distinct criteria, use deterministic checks for verifiable facts, reserve model judgment for nuance, and compare model grades with qualified human judgments before relying on them. When the evidence does not support a decision, let the judge abstain rather than guess.
What makes an LLM-judge rubric reliable?
A rubric is reliable when competent evaluators can apply it to the same task and reach consistent judgments for reasons that match the evaluation’s purpose. Its job is not to make every answer look alike; it is to make the success condition clear enough that valid variations can still pass and meaningful failures can be identified.
Reliability depends on more than the wording of the scoring prompt. The task, the evidence supplied to the grader, the grader itself, and the method used to check its decisions all matter. Anthropic’s evaluation guidance recommends designing tasks for which two domain experts would independently reach the same pass-or-fail verdict. If experts disagree because the request is underspecified, a model judge cannot reliably repair that ambiguity on its own. Anthropic’s evaluation guidance
Think of a rubric as a contract between the task designer and the evaluator: it should say what counts, where to look, how to handle borderline cases, and when there is not enough evidence to decide. That contract should measure the intended capability, not a preferred phrasing, an incidental formatting choice, or one exact path through a task.
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How do I define what the evaluation is judging?
Start by describing the task and the evidence needed to assess its outcome. A grader cannot apply a meaningful criterion if it does not know the request, the relevant context, or what successful performance means.
- State the task. Describe what the model or agent was asked to do, including any constraints that matter to success.
- Identify the judged object. Specify whether the evaluator should assess a final answer, a tool interaction, an external state change, or more than one of these.
- Define success in observable terms. Translate the requirement into evidence an evaluator can point to, such as a required fact in the response or a confirmed change in state.
- Supply the necessary context. Give the judge the task materials and references required to decide. Do not expect it to infer an unstated policy, source, or domain convention.
- Check whether the criterion is stable. Ask whether two qualified people could apply it without inventing different expectations. If not, clarify the task or narrow the criterion.
For example, “give a complete answer” is not a usable criterion until “complete” is defined for the task. One evaluation might require three named items; another might require an answer to every question in a request. The right test is the one that reflects the actual requirement, not a generic impression of thoroughness.
How should I split the rubric into criteria?
Give distinct qualities distinct criteria when they can fail independently. An overall score can summarize performance, but it should not hide whether the problem was incorrect facts, missing coverage, weak sourcing, or another specific failure.
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For a research answer, Anthropic describes rubric dimensions including factual accuracy, citation accuracy, completeness, source quality, and tool efficiency. These are an example for research work, not a universal list to copy into every evaluation. Define dimensions from the task being evaluated. Anthropic’s account of its multi-agent research system
One practical test is whether two criteria could reasonably receive different results for the same output. A response might contain accurate claims but cite sources that do not support them; accuracy and citation accuracy therefore provide different information. By contrast, two criteria that always rise and fall together may be duplicative or poorly distinguished.
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- Use one criterion for one judgment target wherever possible.
- Keep required content separate from style unless style itself affects task success.
- Make critical requirements visible rather than burying them in a broad “quality” score.
- Do not add a dimension merely because it appears in another team’s rubric.
Anthropic reports that, in its research-system work, it experimented with multiple judges and found that one call using one prompt to produce dimension scores and a pass/fail grade most closely matched human judgments in that system. This is a case-specific result, not evidence that a single judge is always better than multiple judges. Choose the arrangement that you can validate for your criteria and use case. Anthropic’s account of its multi-agent research system
How do I write pass conditions and score levels?
Replace impression words with distinctions an evaluator can apply. Terms such as “good,” “strong,” and “complete” do not tell a judge what evidence earns a score unless the rubric defines them.
For a binary criterion, specify what must be true for a pass and what constitutes failure. For a numeric scale, define every level that will be used in practice, not just the highest and lowest scores. A scale is useful only if the evaluator can tell why adjacent levels differ.
| Rubric element | What to specify | Illustrative wording |
|---|---|---|
| Dimension | The single quality under review | “Required information” |
| Evidence | The response, reference, or state the grader may inspect | “The final response and the supplied task requirements” |
| Pass condition | The observable condition that satisfies the requirement | “Every required item is present and accurately represented” |
| Fail condition | The omission or error that disqualifies the result | “At least one required item is missing or materially misrepresented” |
| Insufficient evidence | What the evaluator should do when it cannot decide | “Return Unknown; do not infer the missing fact” |
| Score scale | Observable boundaries for each permitted level | “Pass if all requirements are met; fail otherwise” |
This is a practical synthesis of the evaluation guidance, not a prescribed standard. Adapt it to the task. If a numeric score is needed, add boundaries that distinguish partial success from failure and explain how omissions or errors affect the grade. If a simple pass/fail decision answers the evaluation question, avoid adding a numeric scale that creates extra ambiguity.
Examples can clarify boundaries, especially for recurring edge cases, but they should not accidentally turn one valid wording or approach into the only accepted one. State the rule first; use examples to illustrate its application, not replace it.
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When should I use code, a model, or a human grader?
Choose a grader for each criterion according to the kind of judgment it requires. Use the least subjective method that can make the decision correctly; many evaluations are better served by combining methods than by routing every condition through an LLM.
| Grader | Best fit | Strengths | Limits to plan for |
|---|---|---|---|
| Code-based | Exact, objectively verifiable conditions such as required fields, values, or test results | Fast, reproducible, and straightforward to debug against explicit rules | Cannot reliably judge nuance that is not encoded in the check |
| Model-based | Open-ended work where multiple valid responses require contextual judgment | Can assess nuanced criteria that are difficult to express as exact checks | Judgments can vary; calibration and inspection are needed |
| Human | Reference judgments and audits for subjective or consequential criteria | Provides expert review for cases a rubric or model may mishandle | Takes more time and costs more than automated grading |
For example, code can verify whether a response contains a required field, while a model may be better suited to judge whether an explanation answers an open-ended question. A person can establish or review the reference judgment when the criterion is subjective or the consequences of error are significant. These approaches can be combined: automate what is objectively checkable, use a model where nuance is necessary, and retain human review where it provides meaningful oversight.
OpenAI’s Graders API reference documents configurable grader types including score-model, label-model, text-similarity, string-check, and multi graders. The reference is useful when implementing a grading workflow, but the current configuration details should be checked in the live documentation because interfaces and supported options can change. OpenAI Graders API reference
How do I calibrate an LLM judge against human judgments?
Before using model scores as evidence about system quality, compare them with judgments from qualified people applying the intended rubric. Calibration shows where the model agrees, where it diverges, and whether the rubric itself leaves room for inconsistent interpretation.
- Assemble representative cases. Include ordinary examples and cases that test the rubric’s important boundaries.
- Have qualified reviewers apply the rubric. They should grade the same cases using the same evidence and instructions intended for the judge.
- Run the judge on those cases. Give it the rubric, task context, and supporting evidence it would receive in the actual evaluation.
- Compare decisions criterion by criterion. Record both the scores and the reasoning or evidence cited for them, rather than reducing the comparison to one aggregate number.
- Investigate disagreements. Determine whether the rubric is vague, the judge lacks context, human instructions differ, or the task does not have a stable answer.
- Revise and repeat. Change the task, rubric, or judge input as appropriate, then check whether the remaining differences are understood and acceptable for the intended use.
Anthropic’s January 9, 2026 guidance says LLM-as-judge graders should be closely calibrated with human experts to build confidence that human and model grading do not diverge substantially. Calibration is not a one-time guarantee: continue sampling human reviews after deployment, particularly when scores will inform important decisions. Anthropic’s evaluation guidance
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Anthropic’s 2022 article on model-written evaluations reports that crowdworkers rated generated examples highly relevant and agreed with 90–100% of labels in the described study, which generated 154 datasets. Those figures describe that study’s evaluation-generation work; they are not a general benchmark for LLM judges, a required acceptance threshold, or proof that another rubric is dependable. Anthropic’s 2022 model-written-evaluations article
What should I inspect when a judge gets a case wrong?
Read the actual task, model output, judge instructions, evidence, and grade for a sample of trials. A score by itself cannot tell you whether the model failed, the judge misapplied the rubric, or the task left too much room for interpretation.
Use disagreements and surprising passes to audit the evaluation itself. Common sources of misleading results include ambiguous instructions, unstable environments, grading bugs, missing context, and loopholes that let a system satisfy the literal check without demonstrating the intended behavior. A rubric can also reject a valid alternative if it treats one tool sequence or phrasing as mandatory when the outcome is what matters.
- Model error: The output or resulting state does not meet a clear requirement.
- Rubric ambiguity: The grader has to invent a standard because the pass condition does not settle the case.
- Evidence gap: The required reference or context is absent, so the judge cannot verify the claim.
- Grader defect: The judge overlooks relevant evidence, applies an incorrect rule, or grades inconsistently.
- Evaluation loophole: A response passes the written check without achieving the intended outcome.
Fix the failure at its source. Clarify a vague condition, provide a missing reference, correct a faulty check, or adjust a task that rewards an incidental behavior. Add protection against easy bypasses only when it tests the intended capability rather than formatting or process details that do not matter to success.
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Treat reliability as an ongoing property of the task, rubric, and grader—not a permanent label earned after one successful calibration. A model, tool environment, task distribution, or grading prompt can change, and any of those changes may affect what the evaluation measures.
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Keep capability evaluations and regression evaluations distinct. Capability evaluations probe areas where a system still struggles; regression evaluations check whether behavior that was previously reliable has deteriorated. Review cases that become saturated, investigate new failures, and recalibrate after meaningful changes to the system or tasks.
Pair automated scoring with production monitoring and periodic human review. Automated checks can cover a large body of work, while human inspection can reveal misunderstandings or failure patterns that the rubric did not anticipate. Review transcripts and grades across trials rather than assuming a stable average proves every criterion is working as intended. Anthropic’s evaluation guidance
What is a practical checklist before using the scores?
Use this final review to decide whether the rubric is ready to support the conclusion you intend to draw from it.
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- Does every criterion assess a distinct, relevant quality?
- Are the evidence, pass condition, fail condition, and abstention behavior explicit?
- Are objectively verifiable conditions handled by deterministic checks where practical?
- Can the judge accept different valid answers or tool paths when the outcome is correct?
- Have qualified humans graded representative cases using the same instructions?
- Have disagreements been reviewed and their causes addressed?
- Is there a plan to inspect transcripts, sample human reviews, and recalibrate after changes?
If the answer to a key question is no, the score may still be useful as a signal for investigation, but it is not yet a dependable standalone verdict. Use it accordingly until the task, rubric, and grader have been checked together.
Frequently Asked Questions
Can I use an LLM judge to grade a different model from the one that wrote the rubric?
The cited guidance does not establish a universal rule about which model should write a rubric or judge another model. What matters for confidence in the resulting scores is whether the grader has been checked against qualified human judgments on the tasks and criteria where it will be used.
Does 90–100% label agreement mean an LLM judge is reliable?
No. That agreement figure belongs to a specific 2022 study of crowdworkers rating examples generated for model-written evaluations, not a general test of LLM-judge reliability. It cannot certify a different judge, rubric, or task.
Can a rubric cover several different tasks?
It can share general principles, but task-specific success conditions and evidence still need to be explicit. A criterion that is meaningful for one task may not measure the intended outcome in another.
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