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Don’t require an AI feature to repeat the same wording to pass. Define what a good result must accomplish, test representative cases against those criteria, and compare results across repeated runs and system versions. Keep exact checks for genuinely deterministic requirements—such as valid JSON or a required field—and use rubrics, labels, comparisons, or human review for open-ended qualities.
What should an AI feature pass?
Start by describing the feature’s intended behavior in terms you can observe. For example, a support-answer feature might need to answer the customer’s question, use the supplied policy, avoid unsupported promises, and return a required structure. Those criteria give a test a defined target; without them, a score has no clear meaning.
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Separate requirements into two groups:
- Hard constraints: requirements with a clear pass or fail, such as valid JSON, required fields, a known answer, or correct tool use. Check these with deterministic code when possible.
- Qualities with degrees: qualities such as helpfulness, completeness, or tone, where more than one answer may be acceptable. Score these with a task-specific rubric, a category label, a comparison between answers, or human judgment.
Choose acceptance thresholds before you inspect a new run. For safety-related tests, Google’s guidance recommends setting minimum acceptable metric levels in advance. This makes it harder to change the success criteria after seeing a result. Google’s safety and factuality guidance
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How do you build a useful test set?
Use cases that resemble the feature’s actual use, not just a collection of easy examples. A practical test set combines cases written by people who understand the domain, suitable examples from real use, and deliberately difficult edge cases. Add failures and blind spots as they emerge.
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- Normal cases: common requests and typical input variations.
- Edge cases: incomplete, ambiguous, conflicting, or unusual inputs that the feature may encounter.
- Safety cases: prompts intended to expose harmful, policy-violating, or otherwise risky behavior.
- Held-out cases: examples kept separate from development and tuning, so they provide a more independent check of performance.
Use production examples only when they can be handled appropriately, including any privacy and data-use requirements that apply. For safety testing, Google recommends diverse, application-specific datasets and held-out evaluation. Its Responsible Generative AI Toolkit describes BOLD, a dataset of 23,679 English text-generation prompts for bias benchmarking across profession, gender, race, religion, and political ideology. That dataset addresses a particular bias-testing use case; its size does not measure general AI quality or establish that a system is unbiased. Google’s safety evaluation guidance
A public benchmark can be useful, but it may not reflect the users, context, or risks of your product. Google Cloud notes that task-specific evaluation can reveal information that general benchmarks and public leaderboards do not. Google Cloud’s generative AI evaluation overview
Which grading method fits each test?
Use a grader that matches the criterion. Exact checks are appropriate when exactness is the requirement; they are a poor fit for answers that can be correct in many different ways.
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| What you are checking | Suitable approach | What to watch for |
|---|---|---|
| Strict format or required field | Parser, schema validation, or custom deterministic code | A structurally valid answer can still be wrong or unhelpful. |
| Known fact or answer | Exact match when wording is fixed; otherwise a carefully chosen reference comparison or rubric | Different wording can express the same correct answer, while superficial similarity can hide an error. |
| Quality with several acceptable answers | Task-specific rubric, categorical label, or expert annotation | Define the criteria clearly and review borderline cases. |
| Which of two outputs is better | Pairwise comparison using the same case and stated criteria | A preference is meaningful only when the comparison criteria are clear. |
| Specialized or high-stakes judgment | Review by a qualified human, optionally alongside automated grading | Do not treat an automated grader as ground truth without validation. |
Model-based graders can help assess open-ended answers, but the grader is itself part of the evaluation system. Check whether it follows the intended criteria, and inspect disagreements and borderline examples. OpenAI’s guidance describes pairwise comparison, classification, and scoring against criteria; its dataset documentation covers grader types and expert annotation. Google Cloud documents rubrics, computation-based metrics, and custom functions. OpenAI’s evaluation best practices · OpenAI’s dataset guide · Google Cloud’s evaluation overview
How do you account for answers changing between runs?
Run the same cases against the versions you are comparing, while holding relevant conditions stable. Record the model or model version, prompt, settings, retrieval context, tools, and application configuration. If users experience stochastic answers in production, include that variability in evaluation instead of assuming a deterministic setting represents the live feature.
- Choose the cases and criteria. Use the same test set and the same pass rules for the baseline and the candidate version.
- Repeat cases when variability matters. Save each output rather than retaining only a summary score.
- Calculate task-relevant results. Report measures such as pass rate where suitable, and examine variation for individual cases as well as the aggregate.
- Investigate changes. Review failures, regressions, and cases where runs disagree before deciding whether a change is acceptable.
There is no universal repeat count established for every AI feature. Choose the number of runs based on the metric, observed variation, evaluation cost, and consequences of a mistaken release decision. A single output is only one sample, not a stable characterization of performance.
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NIST’s February 2026 report explains that evaluation metrics are estimates subject to uncertainty and that uncertainty matters when comparing results. It distinguishes a score on a fixed benchmark from performance generalized to a broader population of related items. Without uncertainty quantification, an apparent difference may be due to chance rather than a real difference between systems. NIST also cautions that zero-temperature sampling does not guarantee full determinism. NIST AI 800-3, Expanding the AI Evaluation Toolbox with Statistical Models
How should safety and rare failures be tested?
An average score can conceal a low-frequency failure that matters greatly. Add targeted cases for risky behavior, including indirect or unusual formulations, and inspect the worst outcomes as well as overall metrics. Google recommends application-specific safety datasets and red teaming; its safety guidance notes that multiple rounds may be needed because generated outputs can vary. Google’s safety evaluation guidance · Google’s safety and factuality guidance
Decide in advance whether a safety requirement is a minimum threshold, a hard prohibition, or a quality to monitor. If one dangerous failure is unacceptable, an average pass rate alone is not an adequate release criterion.
When should you rerun the evaluation?
Run the suite when a change could affect the result, including changes to the prompt, model, retrieval, tools, application logic, or surrounding workflow. Compare the new results with an established baseline, investigate regressions, and add newly discovered failures to the test set.
Evaluation also continues after release. Suitable user feedback and production failure examples can reveal cases the original tests missed; use them to improve the dataset and future checks. OpenAI recommends continuous evaluation as systems change and evolving datasets that incorporate edge cases and blind spots. Google’s guidance also recommends planning for monitoring and user feedback because testing and mitigation cannot guarantee perfect behavior. OpenAI’s evaluation best practices · OpenAI’s dataset guide · Google’s safety and factuality guidance
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsHow do you choose an evaluation approach?
For two or more real options—such as a rubric grader versus expert review—compare them against the job the evaluation must do:
- Criterion: Is the requirement exact, or can multiple outputs be acceptable?
- Evidence: Is the best signal deterministic code, a reference, a rubric, expert annotation, or user feedback?
- Coverage: Does the test set include normal use, edge cases, suitable real examples, and adversarial safety cases?
- Repeatability: Are run conditions recorded, and does the comparison account for variability and uncertainty?
- Operational fit: Can the team rerun the suite after changes, inspect individual outputs, and maintain the dataset?
- Risk sensitivity: Does the release decision depend on average performance, worst-case behavior, or a minimum safety threshold?
Evaluation tools can help teams maintain datasets, apply graders or rubrics, compare versions, and inspect results. Their features and availability can change, so verify current product documentation before adopting a particular service. OpenAI’s dataset guide currently states that the Evals platform is scheduled to become read-only on October 31, 2026, and shut down on November 30, 2026; check the current dataset documentation for the latest timeline and migration information.
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