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To choose an AI model for an application, test candidates on the same representative examples, grade them against criteria defined in advance, and compare results by task, risk, and operating conditions—not by a public leaderboard alone. A useful benchmark is an ongoing loop: define the desired behavior, run the tests, inspect failures, improve the system, and rerun the same set.
1. Define the decision before testing
Start with the actual application, not a generic idea of model quality. Write down who will use it, what inputs it will receive, what outputs it must produce, and what makes an answer useful. For example, a support assistant might need to answer from an approved knowledge base, return a valid JSON object, and escalate when the source material does not support an answer.
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Turn those needs into acceptance criteria before looking at candidate results. Separate requirements that every candidate must meet from preferences that can be traded off. For a safety-sensitive use, identify product-specific risks and set minimum acceptable safety levels before assembling the test set. Google’s Gemini safety and factuality guidance recommends setting minimum levels in advance; it notes that worst-case behavior can matter more than average performance for some safety tasks.
As Google puts it: “It’s good practice to think about the minimum acceptable levels of safety metrics before testing so that 1) you can evaluate the test results against those expectations and 2) you can gather the evaluation dataset based on the tests that evaluate the metrics you care about most.”
2. Build a representative evaluation set
Your evaluation dataset should resemble the inputs the application is expected to handle. Use real examples where permitted, carefully authored cases, or a mixture. For tasks with verifiable answers, label the expected result or acceptable outcomes so graders have something concrete to check.
Include ordinary examples as well as cases likely to expose weaknesses. Consider:
- Common traffic patterns and the range of input lengths and phrasings you expect.
- Meaningful user or content subgroups, so an aggregate score does not conceal uneven performance.
- Hard cases, ambiguous requests, and edge cases that matter to the application.
- Relevant adversarial inputs and attempts to elicit unsafe or policy-violating behavior.
When feasible, keep final comparison examples separate from examples used to tune prompts or models. Reusing the same cases for tuning and final evaluation can make results less useful as an independent check. Google’s evaluation guidance calls for diverse, use-case-relevant datasets and discusses held-out data where training overlap is a concern.
Public academic benchmarks can add context, but they do not replace testing the application itself. Google’s guidance lists BOLD at 23,679 prompts, CrowS-Pairs at 1,508 examples, and TruthfulQA at 817 questions across 38 categories. These are counts shown on Google’s evaluation guidance page; they are not claims about the datasets’ original publication years. Public benchmark implementations can differ, and widely used sets can saturate, so treat their scores as signals rather than universal rankings.
3. Choose graders that match the behavior
A grader should measure the property your acceptance criterion describes. A simple automatic check is often more dependable than a subjective score when the expected result is exact; open-ended quality judgments need a different approach.
| Output or quality being checked | Suitable grading approach | Important limitation |
|---|---|---|
| Exact label, required phrase, schema, or fields | Deterministic checks such as string, label, or schema validation | Passing a format check does not establish that the content is correct or useful. |
| Similarity to a reference answer | A text-similarity metric | Use it only when closeness to the reference meaningfully reflects quality; valid answers may be worded differently. |
| Open-ended answer quality | A written rubric, with automated or model-based grading validated against human judgments | Ambiguous or high-impact judgments may still need human review. |
| Qualitative differences between candidates | Human side-by-side review or a comparison tool such as Google’s LLM Comparator | Record the rubric and review conditions so judgments can be interpreted. |
OpenAI’s grader reference documents string-check, text-similarity, score-model, label-model, and multi-graders. The right choice depends on the task: a fluent answer should not pass merely because a judge likes its style if it violates a required format or makes an unsupported claim.
4. Run a fair, reproducible comparison
Give each candidate the same test items, task instructions, output requirements, and application-relevant settings. Record enough detail to explain what was tested and to repeat it: model identifier or version, test date, prompt, generation settings, grader version, dataset version, and run identifier.
Repeat runs when outputs may vary for the same prompt. Google’s safety guidance notes this variability; one run may not represent typical behavior. Keep repeated results identifiable rather than silently combining them, and report the run conditions alongside any summary.
Compare each candidate on the metrics and slices that matter, not just a single aggregate. A practical scorecard can include:
- Task success and output validity.
- Factuality or groundedness, when the application depends on reliable source use.
- Safety and policy compliance, including worst-case results or per-category thresholds where appropriate.
- Fairness across relevant user groups.
- Consistency across repeated runs.
- Cost, latency, context capacity, and deployment requirements under the intended workload.
For operational measures such as latency and cost, define local measurement conditions: workload, settings, and what costs are included. The sources do not establish a provider-neutral protocol or universal weighting formula. If one model is more accurate but slower, for example, state the trade-off and apply the priorities you set before testing rather than inventing a composite score after seeing the results.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Decide with thresholds, slices, and trade-offs
First check whether each candidate meets every must-pass constraint. A strong average should not rescue a model that fails a required schema, falls below a safety floor, or performs poorly on a critical subgroup. Where risk warrants it, compare worst-case behavior or minimum per-category performance rather than relying on the mean.
Among candidates that pass the requirements, use the remaining metrics to make the trade-offs explicit. The best model for an application is the candidate that meets its acceptance criteria under the intended workload—not necessarily the one with the highest score on a general benchmark.
6. Use failures to improve the benchmark
Review failed cases and disagreements between graders. They may reveal a prompt or system weakness, a missing test category, an unclear rubric, or an unreliable automatic judge. Refine the system and, where appropriate, add cases that capture the newly discovered failure mode. Then rerun the same benchmark so the before-and-after comparison stays meaningful.
OpenAI’s Working with evals guide describes evaluation as an iterative way to specify and assess behavior. It currently says the Evals platform is being deprecated: existing evals are scheduled to become read-only on October 31, 2026, with platform shutdown scheduled for November 30, 2026. The guide points new users or people seeking an iterative environment toward Datasets. Because platform dates and availability can change, check the live documentation before selecting a workflow.
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