Test the feature as a complete system—not just a model call. Define the user-visible behavior and risks, build a documented suite that reflects real use, run it against the same application components and conditions you plan to deploy, and compare the results with a baseline. After release, monitor behavior and turn confirmed failures into new regression cases. NIST recommends testing AI systems before deployment and regularly in operation; a passing pre-release evaluation is not a substitute for ongoing measurement.
What counts as an LLM regression test?
An LLM feature’s behavior can change when its model, prompt, retrieval data, tools, orchestration, safeguards, or user-facing environment changes. A model-only score therefore cannot establish that the feature still works for users. Evaluate the relevant application path and record the conditions under which you tested it.
Regression testing is a repeatable comparison: run a stable set of cases against the changed system, compare the outcomes with a known baseline, and investigate meaningful changes. The suite should reflect the feature’s intended use and risks, not just examples that are easy to score. NIST’s AI Risk Management Framework Measure function calls for rigorous testing, uncertainty measures, benchmark comparisons, and documented results.
Define what must remain true
Start with observable acceptance criteria for the user outcome. Specify what counts as correct, incomplete, unsafe, unsupported, or failed. Identify the intended users and tasks, important input variation, system boundaries, dependencies, and the consequences of errors. Map the components in scope, including relevant third-party data or software; these context and impact decisions should inform what you measure and how you make release decisions.
For a multi-step or agentic feature, define both the task outcome and the environment in which the system acts. Preserve the harness, tools, scaffolding, retry policy, and resource budget used for evaluation. OpenAI’s guidance on trustworthy third-party evaluations emphasizes that these conditions shape what an evaluation result can support. A result from one tool setup or budget should not be presented as proof of performance under a materially different setup.
Build a representative, repeatable evaluation set
Assemble cases from product requirements, representative user tasks, known incidents, boundary conditions, and the risks identified for the feature. Keep a stable regression core so you can compare runs over time, then add cases when incidents reveal gaps. Record where test data came from and any reason it may not represent actual use.
Use more than one kind of check where the feature calls for it:
- Deterministic checks: Verify schemas, required fields, tool-call arguments, permissions, and other invariants with exact expectations.
- Reference-based or rubric-based checks: Assess semantic quality when there may be several acceptable phrasings or answers. Define the criteria before scoring rather than treating a single reference string as the only valid output.
- Human review: Use reviewers when stakes, ambiguity, or subtle context make automated scoring insufficient. Record the rubric and how disagreements are handled.
This mix is a practical way to apply NIST’s recommendation to use quantitative, qualitative, or combined measurement as appropriate; it is not a universal scoring recipe. Document the cases, metrics, tools, and evaluation conditions. NIST’s Generative AI Profile cautions against extrapolating system capability from narrow, non-systematic, or anecdotal assessments.
Measure the behavior that matters to the feature
Choose measures that correspond to the product promise and mapped risks. Task completion alone may miss harmful or unsupported answers, while a safety score alone may miss a feature that routinely fails its main job. Track relevant dimensions together and retain error categories so a change in the overall result can be investigated.
| Evaluation dimension | What to check |
|---|---|
| Task outcome | Whether users’ representative tasks are completed as specified, including incomplete or failed outcomes. |
| Safety and policy | Whether the feature handles mapped risks and follows its safeguards in relevant cases. |
| Grounding and citations | Whether evidence supports the answer, the answer preserves important context, and citations point to relevant material. |
| Workflow and tools | Whether the required tool actions, permissions, and multi-step workflow complete correctly. |
| Operational behavior | Latency, cost, or other operational criteria when they are part of the feature’s release requirements and are measured under stated conditions. |
Compare the changed system with a known baseline under comparable conditions. Report the tested cases and configuration, observed uncertainty, and limits on generalization. An aggregate score summarizes only what was measured; it does not prove overall quality or safety. NIST recommends benchmark comparisons and uncertainty measures, but does not set a universal LLM test-set size or pass threshold.
Test evidence when answers make grounded claims
For retrieval, citation, or research features, check the relationship between each claim and its evidence—not merely whether a citation is present. NIST’s agentic evaluation probe work separates three useful questions: faithfulness, whether the source supports the claim; completeness, whether the answer captures the source’s full message; and sufficiency, whether the evidence is strong enough for the claim being made.
When the feature provides cited answers, verify sources and citations during pre-release evaluation and ongoing monitoring. Where practical, preserve an audit trail linking outputs to the evidence used. That makes it easier to distinguish a retrieval failure from an unsupported inference or an answer that leaves out material context. See NIST’s work on building evaluation probes into agentic AI and its Generative AI Profile.
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Run the documented suite whenever a change could affect behavior—for example, a change to the model, prompt, retrieval corpus, tool, workflow, or safeguard. Keep the task suite and conditions comparable across versions unless the evaluation setup intentionally changes; if it does, document the difference before interpreting the result.
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Set acceptable thresholds and escalation rules for the feature’s risks rather than borrowing a generic pass score. Consider relevant quality and risk measures together, including which cases failed and how serious those failures would be. If a risk cannot be measured adequately, document that limitation and account for it in the release decision. NIST recommends using measurement to inform risk management decisions; it does not prescribe universal LLM release thresholds.
For agentic capability claims, describe the task distribution, system and harness tested, budget, and elicitation approach. OpenAI’s evaluation guidance also identifies validity checks such as checking for contamination, evaluation awareness, refusal behavior, and reward hacking. These details help readers understand what the result establishes and what it does not.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Monitor production and feed failures back into the suite
Pre-release evaluation and production monitoring serve different purposes: the former tests a defined system against known cases; the latter helps detect behavior changes and risks in real operating conditions. Monitor functionality and relevant components, track errors and emerging risks, and provide routes for users or affected communities to report problems. Maintain a response process for investigating reports and deciding whether they indicate a product issue, a test gap, or a changed operating context.
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When an incident is confirmed, convert it into a regression case where possible, update the relevant risk assessment, and rerun the suite for affected changes. Reassess whether the evaluation set still represents how the feature is used and whether its grounding or safety assumptions still hold. NIST’s AI RMF Measure guidance calls for regular testing during operation, monitoring, feedback routes, and updated response processes.
Keep a report that makes each result reproducible
For each run, preserve enough information for another engineer to understand the comparison and repeat it:
- Model identity and relevant settings, plus prompts or task definitions.
- Evaluation data and version, including provenance and known representativeness limits.
- Tools, harness, scaffolding, safeguards, and other conditions that affect the tested path.
- Cases and metrics, scoring method, results, uncertainty, and known limitations.
- For agentic evaluations, relevant attempts, retries, time, and token or cost budget, along with validity checks.
- The baseline used, material differences between runs, and the release decision.
Formalized reporting makes it possible to tell whether a changed score reflects a product change, a changed evaluation, or different test conditions. That distinction is essential if the suite is to guide release decisions over time.
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