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AI evaluation scores are useful only when the test measures the task your team cares about. To make them defensible, define the claim first, test representative examples, use auditable scoring rules, calibrate automated graders against human judgments, inspect failures, and report the exact conditions. Treat a score as evidence about a specific evaluation—not as a complete measure of a model’s capability.
What does an AI evaluation score actually tell you?
A score answers the question the evaluation was designed to test, under the conditions in which it was run. It does not automatically establish that a model will perform equally well on different users, tasks, prompts, tools, or workflows.
Before evaluating, write down the claim the result should support. Is the goal to estimate performance on an application task, compare two models for a particular use, test a capability ceiling, or assess a safeguard? These are different claims and require different evidence. OpenAI’s playbook for trustworthy third-party evaluations emphasizes making the claim and tested system explicit.
- Application evaluation: Does the system work for a defined task and user or traffic population?
- Model comparison: Which of the tested options performs better under equivalent conditions?
- Capability or safeguard test: Does the system demonstrate a particular ability or behavior under specified conditions?
State what success means and what decision the score will inform. A result can support a narrow, well-defined claim without supporting a broader one.
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How do you build an evaluation that represents real use?
Choose examples that resemble the intended use, rather than relying on a convenient collection of generic prompts. OpenAI’s evaluation best practices recommends task-specific evaluations and identifies production, domain-specific, human-curated, and historical data as possible sources.
- Include examples from the relevant domain and, where appropriate and permitted, real production or historical cases.
- Add human-curated examples for important edge cases that ordinary traffic may not cover often.
- Keep test examples distinct from development examples where possible, and record failures so the evaluation can improve over time.
- Check that prompts, labels, reference answers, and instructions represent the behavior you want to measure.
A larger test set does not fix a poorly defined task. If an instruction is ambiguous, an answer key is wrong, or the scoring rule rewards the wrong behavior, more examples may only make the flawed result look more precise.
Public or frequently reused benchmarks also have a generalization risk: models may have encountered the tasks during training, or an agent may retrieve answers while being tested. Consider private or newly constructed examples when familiarity would undermine the claim, and check whether the system is reproducing task-specific answers rather than demonstrating the intended ability.
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How should you score model outputs?
Choose a scoring method that matches the quality being assessed. Use deterministic checks when the rule is genuinely objective; use explicit criteria and examples when human judgment is needed. No single grader format guarantees that the intended quality is being measured.
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Exact-match checks or executable tests can be appropriate when there is a clear, verifiable answer. They are easy to audit, but may reject valid alternatives or miss important nuance. Inspect cases where a strict check and a reasonable human assessment disagree.
Make subjective rubrics concrete
For qualities such as helpfulness, clarity, or completeness, define observable criteria and examples for different score levels. If the evaluation informs a go/no-go decision, specify the pass/fail threshold before reviewing results rather than choosing it after seeing the scores.
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Calibrate automated graders against people
Automated graders can handle more examples, but compare their judgments with human expert ratings and review disagreements. LLM graders may favor longer or earlier-positioned answers, and their behavior can vary by task. Where appropriate, use criteria-focused comparisons or pass/fail judgments, control for answer length, and verify that automated ratings align with expert annotations. Human graders also need clear instructions and may disagree; their judgments can take more time to collect.
Evaluate agent runs at the right level
For agents, the final outcome may not explain how the system reached it. Assess intermediate traces and tool use when they matter to the claim, as well as the final result. Anthropic’s guidance on agent evaluations recommends structured rubrics, separating grading dimensions where useful, allowing an “unknown” judgment when evidence is insufficient, and continuing to review transcripts. These practices help expose grading problems; they do not guarantee that an LLM judge matches human assessment.
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Review a sample of tasks, outputs, and grader decisions instead of relying only on an aggregate number. OpenAI’s third-party evaluation playbook says: “A trustworthy report makes those checks visible: evaluators should review samples for these behaviors every time an assessment is run.”
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- Contamination or retrieval: A system may know a public answer from prior exposure or find it during tool-assisted evaluation, rather than demonstrate the target ability.
- Broken or ambiguous tasks: Missing materials, incorrect answer keys, unclear instructions, brittle exact-match rules, or unreliable services can penalize valid behavior.
- Shortcuts and reward hacking: A system may exploit a prompt, scorer, hidden file, or harness without performing the task as intended.
- Refusals: Refusals can affect the measured result or obscure the capability under test. State how they were counted and review representative cases.
- Evaluation awareness: Behavior may change when a system recognizes that it is being tested; consider whether that affects the interpretation of the result.
- Harness mismatch: Tools, budgets, retries, state handling, monitoring, or scaffold constraints can change observed performance, even when the underlying model is unchanged.
Task quality is not a theoretical concern. In a July 8, 2026 audit of SWE-bench Pro, OpenAI estimated that “~30% of the tasks are broken.” That figure applies to the tasks examined in that benchmark-specific audit; it is not an estimate of how many tasks are broken across benchmarks generally. See OpenAI’s coding-evaluation audit.
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A model can appear to lead because of which questions happened to be sampled. Anthropic’s statistical guidance on model evaluations frames comparisons as a question of whether an observed difference reflects a real performance gap or luck in the question selection.
Report the dataset, sample size, scoring method, and appropriate uncertainty alongside a comparison. Do not present a point estimate as conclusive when the measured difference may be sensitive to the sampled tasks. There is no universal sample-size rule or confidence cutoff established by this guidance; the appropriate analysis depends on the evaluation and decision.
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For a practical comparison, check the following dimensions:
- Task and population fit: Does the test represent the intended task and users?
- Validity and contamination controls: Could familiarity, retrieval, or shortcuts explain performance?
- Scoring quality: Are objective checks appropriate, automated graders calibrated, and disagreements inspected?
- Test conditions: Were model version, prompt, tools, harness, budget, and retries equivalent?
- Uncertainty and cost: How stable is the observed difference, and what resources did each run consume?
These are comparison questions, not a universal scoring formula. Their purpose is to reveal whether a numerical difference supports the decision your team needs to make.
What should an evaluation report disclose?
Report enough detail for another team member to understand what was tested and what the score can support. For agentic systems, that includes the setup as well as the result.
- The claim, target task, and intended user or traffic population.
- The tested model and configuration, prompts, elicitation method, and evaluation dataset.
- The harness, tools, budgets, retries, and relevant state or scaffold constraints.
- The scoring rules, grader type, thresholds, and how refusals were counted.
- The sample size, comparison method, uncertainty, and validity checks performed.
- Known limitations, task failures, and how representative examples and grader disagreements were reviewed.
OpenAI’s evaluation playbook emphasizes describing the claim, tested system, elicitation, and evidence that validity hazards were checked. A bare score without this context is difficult to interpret or reproduce.
How do you keep evaluation results useful after release?
Evaluation is ongoing because models, prompts, tools, safeguards, and workflows change—and new cases appear in use. OpenAI recommends continuous evaluation, while Anthropic’s agent-evaluation guidance discusses using failures and feedback to improve task sets.
Quick Recap
- Run relevant checks when a model, prompt, tool, safeguard, or workflow changes.
- Monitor the application for new failure patterns and review user feedback and logs appropriately.
- Add useful, representative failures to the evaluation set, while keeping test and development data appropriately distinct.
- Revisit whether the evaluation still supports the decision and claim for which it was designed.
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