Build an AI evaluation scoreboard around one defined business task—not a generic model-quality number. Use representative test cases, task-specific measures and explicit launch gates; combine deterministic checks with calibrated human or model grading; and rerun the same evaluations when the system changes. The result should show what passed, what failed and where evidence is uncertain, not imply that a single score proves an AI system is ready.
Start with the task and the decision
Write down what the AI system is expected to do, who will use it, where it fits in the workflow and what decision the evaluation will inform. Define success in terms of the organization’s outcome, and identify plausible harms if the system fails.
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Evaluate the application people will actually use: its model, prompts, retrieval, tools, interface and operating process. A public model leaderboard cannot measure all of those components. NIST’s TEVV-Athlon draft frames assessment as adaptable to different AI applications and organizational objectives; NIST describes the resource as an initial public draft, and its page does not establish whether a later version has been issued. Read NIST’s TEVV-Athlon framework page.
Build a test set that resembles real use
Start with realistic inputs, such as production examples or user feedback when you can use them appropriately. Add domain-expert examples with expected labels, reference answers or rubric annotations. Include ordinary requests as well as edge cases and adversarial inputs.
- Document how cases were selected and what parts of expected use they represent.
- Keep a held-out set for comparisons so that changes are not judged only on examples used during development.
- Add informative failures and newly discovered blind spots over time.
- Use suitable authorization and handling controls before including sensitive production data; there is no single data-governance recipe that fits every company.
OpenAI’s dataset guidance describes evaluation datasets as dynamic and recommends expanding them as edge cases and blind spots emerge. Its Evals guide also shows how examples can pair an input with human-provided ground truth. See OpenAI’s guide to getting started with datasets.
Choose measures and gates for the task
Give each measure its own row or panel rather than blending unlike qualities into one opaque score. Select only dimensions that matter to the task, define each in observable terms and explain the threshold that triggers action.
- Task success or correctness: Did the system complete the requested job or return the right label or result?
- Factual accuracy and grounding: Are claims correct and supported by the source material, if the system uses documents?
- Completeness and instruction following: Did it cover required information and obey relevant constraints?
- Format or schema validity: Does the output meet machine-readable or workflow requirements?
- Safety and policy behavior: Does it handle prohibited, sensitive or risky requests as intended?
- Robustness: Does performance hold across meaningful edge cases and user or input slices?
- Operational measures: Are latency and cost acceptable for the intended workflow?
For each measure, distinguish a launch gate from a monitoring signal or improvement target. A useful scoreboard can display the observed result, sample size or test-set version, pass threshold, change against a comparable baseline, representative failures and an owner or next action. These are practical design choices, not a universal mandated layout or metric list.
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Published example thresholds are illustrations, not company-wide targets. OpenAI’s evaluation best-practices page gives one example using 1,000 held-out transcript-summary examples, with ROUGE-L of at least 0.40 and coherence of at least 80% using G-Eval. It separately illustrates a Q&A setup with context recall of at least 0.85, context precision over 0.7 and more than 70% positively rated answers. Those figures are examples on a page whose publication date is not stated; set thresholds from your task, risk and baseline instead. Read OpenAI’s evaluation best practices.
Match the grader to the question
Use deterministic checks when an answer can be verified objectively: exact string or label matching, schema validation, required-content checks or code-based rules. For subjective qualities, use a human rubric or an LLM grader with explicit criteria and examples.
Define what low, middle and high rubric scores mean with concrete examples. For consequential decisions, retain a pass/fail gate alongside any numerical rating. Before scaling automated grading, compare it with human annotations, review disagreements and inspect false positives and false negatives. Recheck calibration when the task or rubric changes.
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LLM judges can be affected by position and verbosity bias. OpenAI’s guidance notes that pairwise comparisons or pass/fail grading can be more reliable for suitable tasks than open-ended scoring, but no grading method should be trusted without validation against the decision it is meant to support.
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When comparing prompts, models, retrieval settings or other components, run the same cases with the same criteria. Record both the system version and evaluation-set version for every run. Where practical, use paired or blinded comparisons, then inspect individual failures instead of treating a small aggregate difference as decisive.
Compare relevant dimensions such as task success, severity of failures, robustness by case slice, grounding, safety, latency and operating cost. Include sample size or uncertainty where available, and investigate meaningful regressions in a particular slice even if the overall result improves. These comparisons are most useful when they expose trade-offs rather than announce a universal winner.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Add evidence checks for document-based answers
If an AI answers from documents or an agent makes factual claims, evaluate the connection between each claim and its cited or retrieved evidence. Where the system and risk justify it, retain a reviewable link between claim, evidence and evaluation result.
NIST’s evaluation-probe project describes comparing claims with a human-curated corpus and recording an audit trail. Its example dimensions are faithfulness (whether the source supports the claim), completeness (whether the answer captures the source’s message) and sufficiency (whether the evidence carries the claim’s burden). The project page describes ongoing research, not a universal certification or finished commercial product. Read NIST’s project page on evaluation probes for agentic AI.
Make the scoreboard part of the release cycle
Run evaluations during development and whenever a relevant component changes. Use real-world feedback and newly observed failures to improve the test set; check for nondeterministic behavior where it matters. Assign an owner for the dataset, rubric, scorecard and launch decision so findings lead to action.
OpenAI describes continuous evaluation as running checks on changes and growing datasets as new cases arise. Its Evals documentation also states that existing eval content is scheduled to become read-only for existing users on October 31, 2026, with shutdown scheduled for November 30, 2026; the documentation recommends considering Datasets as a more iterative starting point. These are dated, platform-specific notices, so check the official page before making a migration or procurement decision. Check OpenAI’s current Evals documentation.
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