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LLM testing differs because the same input can produce different outputs, and a correct answer may not match one exact reference string. Reliable evaluation combines ordinary software tests with representative examples, task-appropriate graders, human review where judgment matters, repeatable comparisons, and end-to-end checks for tools and agents.
Why ordinary software tests are not enough
Traditional unit tests often check whether a fixed input produces a precise, deterministic result. Those tests still matter for an LLM-powered application: use them for parsing, routing, API contracts, permissions, and deterministic business rules. But they cannot, by themselves, tell you whether generated language is useful, accurate, or appropriate.
Generative models can respond differently to the same input, and a valid answer may express the right meaning in different words. OpenAI’s evaluation guidance describes this variability as a reason traditional software testing methods are insufficient on their own for AI architectures. The practical change is not to abandon exact tests; it is to choose an evaluation method that matches the behavior being tested.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstall| Testing question | Conventional software test | LLM evaluation |
|---|---|---|
| What counts as a pass? | Often an exact value, state, or contract. | May be an exact value, semantic criterion, rubric, safety requirement, or successful workflow. |
| How is the expected result represented? | A fixed expected output is often sufficient. | A reference answer can help, but open-ended tasks may need criteria rather than one canonical response. |
| What can cause a failure? | Code, configuration, or dependency behavior. | Those factors, plus model variation, prompt or context changes, retrieved content, tool use, and multi-step decisions. |
| What does a test result establish? | That the tested behavior met its assertion under the tested conditions. | That the system met the selected criteria on the tested examples and conditions—not a guarantee for every user or input. |
Exact assertions remain suitable for constrained outputs, such as a required field value or a permitted label. For a summary, explanation, or tone requirement, a string comparison can mark a sound response as a failure simply because it is phrased differently. Conversely, a response that resembles a reference can still miss an important requirement.
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How to choose what counts as a good answer
Start with the property you need to measure, then select a grader that can meaningfully assess it. OpenAI’s published grader guidance describes string checks, similarity comparisons, model-based scoring, and code-based graders. These are different instruments, not interchangeable definitions of quality.
- Exact or code-based checks: Use these for objectively verifiable requirements, such as valid structure, a required field, a calculation, or a business rule.
- Similarity checks: Use them when closeness to a reference is informative, while recognizing that a similar answer is not automatically correct and a differently worded answer may still be right.
- Rubric or model-based scoring: Use a clear rubric for judgment-based properties such as completeness or adherence to instructions. Inspect examples to see whether the grader applies the rubric as intended.
- Human review: Use expert judgment when a criterion is subtle, consequential, or not reliably captured by an automated check. Human annotations can also help identify where an automated grader disagrees with the intended standard.
OpenAI documents grader scores on a 0-to-1 scale in its grader interface; that range describes the interface, not a universal quality measure. A score only means what the chosen grader and rubric make it mean. Review borderline cases and clear failures, and do not treat an automated judge as ground truth without checking its judgments against the criterion.
Build an evaluation set that represents real use
An evaluation can only speak to the examples and conditions it covers. A small set made up of easy or unusually polished prompts may produce reassuring scores while missing common user tasks, ambiguous requests, edge cases, and important failure modes.
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- Add edge cases and examples of known or consequential failure modes.
- Write down the desired behavior or reference answer when one is genuinely useful.
- Use expert annotations for criteria that depend on domain knowledge or careful judgment.
- Keep a stable core set for comparisons, then add cases as new failure patterns emerge.
OpenAI’s evaluation guidance recommends developing datasets over time and incorporating annotations. Treat results as evidence about performance on that set—not as a universal score for the model or an assurance about every production interaction.
A repeatable workflow for testing an LLM application
- Define observable behavior. Translate product requirements into criteria a reviewer or grader can assess. Separate objective checks from judgment-based ones.
- Assemble representative cases. Cover normal usage, important edge cases, and known risks. Add references or expert annotations where they help establish the desired behavior.
- Match each criterion to a grader. Use exact or executable checks for objective properties, and semantic or rubric-based assessment for open-ended ones. Use more than one kind of check when a task has distinct requirements.
- Validate the graders. Review sample outputs, including borderline cases. Check whether automated judgments align with the rubric and expert expectations; revise a vague criterion or unreliable grader rather than trusting the score.
- Record what changed. Track the prompt, model, workflow, dataset, and evaluation criteria for each run. Compare changes using the same cases and as consistent conditions as practical.
- Investigate regressions and successes. Compare scores, then inspect the underlying examples to understand what improved, what worsened, and whether the grader is measuring the intended behavior.
- Refresh the set deliberately. Add cases when real usage or a newly observed failure reveals a gap, while retaining a stable core so results remain comparable over time.
Keeping the task set and criteria steady makes a prompt, model, or workflow comparison more interpretable. It does not remove every source of variation, so record the evaluation conditions and read scores alongside failure examples.
Why agents need workflow-level tests
For an agent, the final response is only part of the result. Tool selection, retrieved information, guardrails, handoffs, and actions taken along the way can determine whether the task succeeds. A correct-sounding final answer does not prove that the agent used the right tool or stayed within its permissions.
OpenAI’s agent evaluation guidance describes a trace as the end-to-end record of model calls, tool calls, guardrails, and handoffs for one run. Traces help diagnose a specific run; repeatable dataset evaluations help compare workflow versions. When the requirement concerns an action or a multi-step outcome, include end-to-end cases that check that outcome, not just the model’s text.
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Applications that browse or use tools can encounter untrusted instructions in retrieved pages or tool output. Include cases that check whether such content can redirect the agent, expose information, or trigger actions outside the intended task. Evaluate the boundaries that matter to the application, including what information a tool can access and what actions it is allowed to take.
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The GPT-5 system card describes vendor-specific evaluations involving prompt injection through browsing and tool output. That is an example of a threat category worth testing, not evidence that any system is generally secure. Prompt-injection tests are one part of a broader security approach; a passing result on a limited test set is not a complete security assurance.
How to compare two evaluation approaches
When deciding whether an evaluation setup is useful—or comparing two setups—make the scope and evidence explicit. These axes are a practical synthesis of published guidance, not a formal standard prescribed by one source.
| Axis | What to report |
|---|---|
| Task coverage | Which user tasks, input types, and edge cases are represented. |
| Criterion fit | Whether success is judged by exact match, meaning, a rubric, safety behavior, or workflow outcome. |
| Evidence quality | Whether the set includes expert labels, useful references, reviewed examples, or automated graders checked against the intended criterion. |
| Repeatability | Whether the same cases and criteria can be rerun with prompt, model, and workflow versions recorded. |
| System scope | Whether the evaluation covers only a model response or also retrieval, tools, guardrails, handoffs, and final actions. |
| Operational relevance | Whether cases reflect production inputs and observed failure modes. |
There is no single metric or universal test suite established for every LLM application. The useful result is a body of evidence tied to a specific task set, criterion, grader, and system version, with enough examples to understand what the score conceals.
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Platform tooling is separate from the testing principles
OpenAI documentation describes a transition for its Evals platform: existing evaluations are scheduled to become read-only on October 31, 2026, and the platform is scheduled to shut down on November 30, 2026. Those dates concern that specific platform, not a change to the underlying methods for testing LLMs; verify the current official deprecation notice before making implementation plans.
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