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To find out whether an AI answer is correct, first decide what “correct” means for the task: matching an expected value, following an instruction, stating verifiable facts, or being useful in context. Then choose a test that measures that property. No single score proves an LLM is generally reliable—or that a particular answer is safe to trust.
OpenAI notes that “Generative AI is variable. Models sometimes produce different output from the same input, which makes traditional software testing methods insufficient.” That makes repeatable evaluations valuable, but only when the test matches the claim you want to make.
1. Compare answers with a reference or metric
For constrained tasks, compare the model’s output with an expected answer. Exact-match and string-match checks can tell you whether the output is identical to a target; for structured output, you can also test whether the required behavior actually executes. OpenAI’s evaluation best practices lists exact match, string match, ROUGE/BLEU, function-call accuracy, and executable evaluations as examples of metric-based checks.
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2. Ask people to review the answer
Human review is useful when judging an answer requires context, nuance, or a decision about whether it is helpful. Reviewers can assess qualities that a simple metric may not capture, but the process takes time and money, and reviewers can disagree. Microsoft Research’s LLM-Rubric paper also notes that human judges do not fully agree.
Make a review more consistent with a scorecard that defines each criterion, includes examples for score levels, and sets pass/fail thresholds as well as numerical ratings. OpenAI recommends refining the scorecard over multiple rounds and aggregating judgments rather than relying on one reviewer’s score. Human review is an important anchor for evaluation, not an infallible ground truth.
3. Use an LLM as a judge
A model grader can compare two answers, score one against explicit criteria, or check an answer against a reference. It can help scale a clearly specified rubric, but its verdict is still a model output—not independent proof that the answer is correct.
OpenAI recommends comparison or pass/fail judgments for greater reliability and advises validating a model judge against human labels before optimizing for cost or latency. Watch for position bias, where the order of responses affects the result, and verbosity bias, where a judge favors a longer response. A clear rubric and balanced presentation can help, but OpenAI cautions that “No strategy is perfect.”
4. Check factuality against a domain-specific corpus
If factual support is the property you need to test, compare claims with a source corpus that represents the domain. A set of questions sampled only from what a model happens to say may fail to probe the facts or errors you care about.
The 2024 EACL paper “Generating Benchmarks for Factuality Evaluation of Language Models” introduces FACTOR (Factual Assessment via Corpus TransfORmation). It transforms a factual corpus into true statements and similar but incorrect alternatives. The authors report that benchmark scores and perplexity do not always rank models the same way; when they disagreed, human annotators found the benchmark score more reflective of factuality in open-ended generation.
A corpus-based benchmark is still bounded by its source material, item quality, and task design. A strong score supports a claim about the covered domain and test—not factual reliability everywhere.
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Before trusting a result, inspect how the test was built and scored. OpenAI’s playbook for trustworthy third-party evaluations identifies risks including contaminated test data, ambiguous or incorrectly scored questions, unsolvable or broken tasks, unintended shortcuts, reward hacking, refusals, and strategic underperformance.
Review the benchmark items, evaluation harness, scoring rules, available tools, and budget. Then inspect samples of apparent successes and failures. A useful report explains what claim the setup is meant to support, how the test represents that claim, and what changed between runs. Standardizing a setup helps comparisons only for the claim that setup was designed to test.
Choose tests by the claim you need to trust
Different checks measure different properties. Use the combination that fits the task rather than treating a single score as a universal ranking.
| Method | Best suited to | What it depends on | What it can miss |
|---|---|---|---|
| Reference or metric check | Exact outputs, structured behavior, repeatable regression checks | An expected answer, metric, or executable condition | Equivalent wording, nuance, or errors in the reference |
| Human review | Context, usefulness, and nuanced criteria | Reviewers, a scorecard, and an aggregation process | Disagreement; the time and cost of review |
| LLM-as-judge | Scaling a defined comparison or rubric | A judge model and criteria validated against human labels | Position and verbosity bias; imperfect judgments |
| Domain-specific factuality benchmark | Factuality within a defined subject area | A representative source corpus and well-designed items | Facts and domains outside its coverage |
| Evaluation validity review | Determining whether a result supports its stated claim | Inspection of items, harness, tools, scoring, and run conditions | Risks not exposed by the reviewed samples or setup |
For a practical evaluation, start by naming the property you care about: exactness, factual support, instruction following, executable behavior, or contextual usefulness. Use deterministic checks where a condition is directly testable; bring in human judgment for nuanced criteria; and use model graders to scale a rubric only after checking their agreement with people. For factuality, choose a corpus that reflects the target domain.
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Keep difficult, rare, and adversarial examples in the test set, and rerun evaluations when the model or surrounding system changes. OpenAI recommends representative, edge, and adversarial examples, expert human labeling, and continuous evaluation as systems change.
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Make comparisons reproducible—and keep conclusions narrow
When comparing two systems, hold the setup consistent and report the details needed to interpret the result: model and system configuration, data, prompts, tools, harness, scoring rules, and review procedure where relevant. OpenAI’s third-party evaluation guidance explains that these choices affect how a result can be interpreted and generalized.
A benchmark score describes performance under its particular data and conditions. It does not, by itself, establish a universal model ranking or guarantee that an individual answer is correct. To decide whether a specific answer is trustworthy, check the claims and consequences that matter for that answer.
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