There is no universal teacher—or single definition—of a good AI answer. The people responsible for a particular use have to decide what success means, drawing on domain experts, evaluators, users, and others affected by the system. They then turn those expectations into criteria, test examples, and evaluation procedures. A high score matters only if the test measures the outcome people actually need.
Who decides what “good” means?
It depends on the task. A useful answer from a medical assistant, a customer-support bot, and a legal research tool will not be judged by the same standards. Accuracy may be essential in all three, but so may different requirements for citing evidence, asking clarifying questions, communicating uncertainty, protecting privacy, or avoiding unsafe advice.
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The model developer can propose evaluation criteria, but cannot settle every value judgment on its own. The people who understand the work should help define what competent performance looks like. Users and people affected by the system can reveal needs and risks that developers or domain specialists might miss. The right mix depends on the use and its consequences.
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How do expectations become something testable?
A team first defines the intended outcome, then decides how to tell whether the system achieved it. OpenAI’s evaluation guidance frames the starting question as, “What’s the success criteria for the eval?” It recommends choosing suitable data and metrics, comparing results, iterating, and evaluating continuously as the system changes. The documentation’s examples are starting points, not universal thresholds.
Write criteria for the actual job
Criteria should describe observable qualities of a response, not merely broad aspirations. For example, a rubric might ask whether an answer is factually supported, addresses the user’s question, uses relevant conversation context, and communicates necessary uncertainty. A high-stakes task may require additional checks for safety or escalation. OpenAI’s guidance describes a target answer as one that is precise, recalls context when needed, and satisfies the user’s need; what that means in practice still has to be specified for each use.
Build examples that represent real use
Test cases should reflect the questions people actually ask, including difficult, ambiguous, unusual, and edge-case requests. A small or unrepresentative set can reward a system for doing well on the test while missing common failures in use. Teams should also be clear about whether examples are intended to measure a fixed set of tasks or support an estimate about a larger population.
Use rubrics to make judgment inspectable
A rubric records what evaluators are looking for and helps explain why an answer passed or failed. It can make subjective judgments more consistent, but it does not make them automatically objective: the criteria themselves still need scrutiny, and different reasonable evaluators may disagree.
OpenAI’s HealthBench illustrates a domain-specific approach. Its benchmark was created with 262 physicians who had practice experience in 60 countries. It includes 5,000 realistic health conversations, each with a physician-created rubric, and 48,562 unique rubric criteria; model-based grading compares answers with those criteria. These are counts describing OpenAI’s benchmark—not proof of universal medical consensus or independent validation of every criterion. OpenAI’s HealthBench page describes the design.
For work tasks, OpenAI’s GDPval evaluation uses occupation-specific tasks and detailed scoring rubrics. Experienced professionals blindly compared and ranked model and human deliverables. OpenAI says its experimental automated grader estimates expert judgments; it is not a replacement for expert graders. That distinction matters: automation can help scale evaluation, but agreement with a grader is not the same thing as a human expert’s considered assessment.
What does a benchmark score actually tell you?
A benchmark score reports performance under a particular test’s conditions. It does not, by itself, establish how well a system will work for every user, task, or setting. To interpret the number, readers need to know what was tested, how answers were scored, and what population the evaluation is meant to represent.
Evaluation can also be sensitive to choices that appear minor. Anthropic reported that simple formatting changes produced about a 5% change in MMLU accuracy in its tests. It also identified concerns including possible training exposure to benchmark material, inconsistent implementations, and errors or unanswerable items. That reported change is an observation about Anthropic’s tests, not a general effect size for all benchmarks. Anthropic’s discussion of evaluation challenges explains why implementation details and test quality matter.
- Fixed-benchmark accuracy: How well did the system answer this defined set of test items under these conditions?
- Generalized accuracy: How well is it expected to perform across a wider population of similar questions?
- Real-world usefulness: Does the system help people achieve the intended outcome in actual use, including relevant safety and communication requirements?
These measurements can inform one another, but they are not interchangeable. A model can do well on a benchmark without demonstrating reliable performance in the field; likewise, a single field observation cannot establish performance across all relevant situations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When are automated scores not enough?
Automated tests are useful for repeatable checks and for tracking changes, but not every evaluation objective can be met by an automated benchmark. NIST’s January 2026 initial public draft on automated benchmark evaluations notes alternatives including red teaming, human-subject experiments, field testing, and post-deployment monitoring. The document is a draft, not final guidance. NIST’s AI 800-2 publication page identifies its status and scope.
Choose additional methods when they answer a question the benchmark cannot. Red teaming can probe for failures or misuse; human-subject studies can examine how people interpret and rely on outputs; field testing can reveal effects in a real workflow; and monitoring can identify problems that emerge after launch. Which methods are warranted depends on the task, the stakes, and the limits of the automated test.
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How should teams keep the definition of “good” current?
Evaluation should be repeated when the model, product, users, or task changes. A new model version can behave differently; a revised workflow can introduce new failure modes; and feedback from real use can expose gaps in the original criteria. OpenAI’s evaluation workflow recommends continuous evaluation rather than treating a one-time score as permanent assurance. For consequential uses, teams should also keep track of what the evaluation does not measure and who has reviewed its criteria.
The practical standard is not one score that declares an AI “good.” It is a documented set of task-specific expectations, tested with appropriate examples and a suitable mix of automated and human judgment, with the result interpreted within its stated limits.
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