AI evaluation data determines what an application test can tell you about model quality. A carefully scored collection of unrealistic or repeatedly tuned examples can produce an impressive number while revealing little about real user performance. The dataset’s scope, provenance, and relationship to development are as important as the metric.
A strong evaluation separates examples used to improve the system from examples reserved to test whether the improvement generalizes. This guide focuses on application-level evaluation and evidence management, not claims about whether a model has seen a particular public document during pretraining.
Define the task and decision first
State what the evaluation should establish. Examples include accurate support classification, grounded answers, correct structured fields, or safe tool selection. Each task needs different reference data and scoring rules. A general helpfulness score may not capture a specific consequential error.
Define who will use the result and what rollout decision it supports. A small offline regression test and a broad launch assessment can have different requirements. Avoid selecting a metric only because a dashboard makes it easy to calculate.
Write the acceptance criteria before reviewing the new system’s output where practical. Otherwise the rubric can drift toward whatever the latest model happens to do well. Keep meaningful failure categories visible rather than compressing every result into one average.
Cover the real input distribution
Include representative successful tasks, ambiguous requests, missing information, unsupported requests, and relevant error cases. Draw from approved production observations, domain experts, or well-designed synthetic examples. Synthetic data is useful, but it should not simply imitate the prompt’s preferred wording.
Consider language, document length, product version, tenant context, and accessibility needs when they affect behavior. A dataset dominated by short polished English questions may miss the failures users experience with noisy or multilingual inputs.
Document coverage gaps. No finite evaluation proves correctness for every future input. A clear statement of what was tested and what remains uncertain is more useful than presenting a benchmark as a universal quality guarantee.
Separate development from held-out testing
Use a development set to inspect failures, refine prompts, and compare ideas. Keep a held-out set for a more independent check of selected changes. Repeatedly studying its exact failures and tuning specifically for them weakens its role as a fresh test.
Prevent near-duplicate cases from crossing the intended split. Several paraphrases of the same source example can make performance appear to generalize when the system has effectively been developed against the same underlying case. Group related examples according to the task’s leakage risks.
For time-dependent products, consider whether a temporal split better represents new work. Document the split method and why it matches the deployment decision. A random split is not automatically wrong, but neither is it automatically sufficient.
Keep references out of the application’s inputs
Ensure the runner supplies only the information the production system would legitimately receive. A reference answer, gold label, or scoring note accidentally included in a prompt can make the test meaningless. Inspect rendered requests, not only the dataset schema.
Retrieval evaluations require particular care. If the intended task is answering from documents, approved answer-bearing documents may properly belong in the corpus. But a hidden answer key or evaluator explanation should not be added merely to make the benchmark easier. Define this distinction explicitly.
Tool-use cases should similarly separate simulated tool outputs from expected actions. The application can receive the approved scenario’s observations, while the evaluator retains the reference behavior. A fake tool response that directly instructs the desired action can hide a weak decision process.
Review privacy and source rights
Use data you are authorized to process for the evaluation purpose. Remove unnecessary personal information and secrets, and consider whether redaction changes the task. A support example can preserve its relevant decision structure without retaining a customer’s actual account credentials.
Review where datasets, model outputs, and grader inputs are sent. An evaluation provider can receive more information than the production application if a runner attaches reference notes or complete records. Apply approved access, retention, and sharing rules to that path as well.
Keep provenance records without creating another sensitive archive. Record source category, collection date, permissions, transformation, and reviewer when useful. Dataset ownership should include who may add cases and who may export them.
Make scoring reproducible and useful
Write a rubric that distinguishes correct, partially correct, unsupported, and harmful outcomes where relevant. For deterministic fields, exact or structured checks may work. For more subjective tasks, calibrated human judgments or a reviewed model grader can help, but their limitations need testing.
Validate the grader against representative human judgments. A grader can favor verbose answers, miss subtle factual errors, or be sensitive to presentation. Do not assume agreement because it produces a detailed explanation. Check consistency and investigate disagreements.
Keep the scoring configuration versioned with the dataset and application configuration. If the rubric changes, comparisons across runs may no longer mean the same thing. Report the relevant version boundary instead of joining incompatible numbers into one progress chart.
Use evaluations as a continuing workflow
Run regression cases when prompts, models, retrieval, tools, or policies change. Add newly observed failures through a reviewed process and decide whether they belong in development, regression, or a future held-out assessment. Growth should improve coverage, not merely inflate case count.
A practical example is a support classifier tuned on common billing tickets. Reserve new ticket families and ambiguous cases for testing, keep customer details synthetic, and verify that gold labels never enter the classifier prompt. Then report category-level failures alongside aggregate accuracy.
After rollout, monitor real behavior within approved privacy boundaries. Offline data can miss distribution shifts and novel workflows. Use production findings to inform the next evaluation cycle without pretending that continuous tuning leaves the old held-out set forever independent.
Frequently asked questions
Can I tune repeatedly on the same test set?
You can, but it becomes development evidence rather than a fresh independent assessment. Keep that distinction clear and maintain suitable held-out coverage.
Is synthetic data always enough?
No. Its usefulness depends on realism and task coverage. Combine sources according to the product’s risks and authorized data availability.
Where should I start designing an evaluation?
Read OpenAI’s evaluation best practices. For evaluating document-grounded answers specifically, see our RAG evaluation guide.
