Before launch, evaluate the complete AI application—not just its base model—on representative tasks and deployment-like conditions. Set acceptance criteria in advance, measure task success, latency percentiles, cost per successful task, safety and security risks, and operational readiness. There is no universal pass score: the right release decision depends on intended use, users, likely harms, and the consequences of failure.
What should an AI pre-launch evaluation establish?
An evaluation should give release approvers evidence that the system is fit for its stated purpose and that its known risks can be managed in the environment where it will run. Start by defining the decision the evaluation must support: launch, limited rollout, more testing, or no launch.
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Before choosing metrics, document:
- Intended use: what tasks the system is meant to perform, and what it must not do.
- Users and setting: who will use it, where, and under what operating conditions.
- Failure consequences: who could be harmed, how severe the impact could be, and what happens if the system is wrong or unavailable.
- Uncertainty behavior: when it should abstain, ask for clarification, hand off to a person, or use another safe fallback.
- Release criteria: what evidence and risk level are acceptable for this specific use.
NIST’s AI Risk Management Framework (AI RMF) treats risk management as a lifecycle activity, from planning and development through deployment and operation. Its trustworthiness characteristics do not carry equal weight in every context, and tradeoffs may arise. Use it to shape a context-specific evaluation, not as a universal scorecard or threshold.
How do you build a representative test set?
Test the application people will actually use: the model together with its prompts, policies, retrieval sources, tools, interfaces, and relevant application logic. A base-model benchmark or a polished demonstration cannot establish how that full system will behave in production.
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Create a versioned set of cases drawn from expected use. Include ordinary tasks, boundary cases, difficult inputs, and high-impact scenarios. Where relevant to the risks, test distinct user groups, languages, contexts, or input types so that an aggregate score does not hide uneven performance.
For a generative system, cases can include realistic prompts, expected answers or behaviors, refusal and escalation scenarios, retrieval or tool interactions, and adversarial probes for features the application actually has. Keep development examples separate from a held-out evaluation set where practical. Use objective checks for deterministic requirements, such as required fields or successful tool calls, and human review or validated graders for qualities that need judgment.
Record enough detail for someone else to interpret or repeat the evaluation:
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- Test data version, case-selection method, and important slices.
- Sampling conditions, grader or reviewer method, and how disagreements were handled.
- Results, uncertainty, known limitations, and any conditions not represented by the tests.
OpenAI’s evaluation guidance emphasizes using test data representative of expected inputs. NIST’s Generative AI Profile likewise calls for performance or assurance criteria to be demonstrated under conditions similar to deployment and documented. Neither a benchmark result nor a pre-launch test guarantees production performance.
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Which quality and task-success metrics should you use?
Define success in terms of the user’s task, not a generic notion of “accuracy.” Select measures that reflect what the application must do and the errors that matter. Depending on the use case, assess correctness, completeness, grounding in approved sources, instruction following, consistency, successful tool or action execution, and appropriate abstention.
Specify how each measure is scored before comparing systems. Use deterministic checks where possible. For subjective judgments, use trained human reviewers or an automated grader whose agreement with human judgments has been checked on a human-labeled sample. NIST recommends empirically validated methods for evaluating capability claims; an unvalidated automated judge should not be treated as a reliable release gate.
Report both aggregate performance and results for important slices. If the system serves different users or contexts, examine whether failures cluster in one group or scenario. Compare candidate systems on the same test set and under the same scoring method; otherwise, an apparent improvement may reflect a changed test rather than a better system.
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Measure end-to-end response time with representative prompts, output lengths, tools, concurrency, network paths, and service tiers. Include both typical and tail behavior: at minimum, report median (P50) and a high percentile such as P95, along with error and timeout rates. Choose additional percentiles if they help describe the service commitment.
For streaming interfaces, measure time to first token as well as total completion time. The first visible output affects perceived responsiveness; total duration matters for finishing the task. Keep workload slices, such as long-context or tool-heavy cases, visible rather than allowing them to disappear into an overall average. Output size and reasoning can affect request duration, while input size and workload conditions can affect time to first token.
Set latency targets from user needs or the service commitment, not from a vendor benchmark measured under different conditions. Record the test workload and environment alongside each percentile so readers can tell what the numbers represent.
How do you compare cost fairly?
Estimate the cost of delivering a successful task under a realistic task mix, not only the token price of one prompt. Depending on the system and billing model, include input and cached input, output, billed reasoning tokens, retries, tool calls, multiple completions, and application services that materially affect the unit cost.
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For a fair comparison, keep the task mix, application configuration, and minimum quality and safety criteria as consistent as possible. Token prices alone are not enough: tokenization and generated quantities can change total cost, and a lower-cost option that fails the required quality or safety bar is not a successful optimization.
How do you evaluate safety, security, and failure behavior?
Turn the risks identified for the application into test scenarios. Depending on the system, these may cover unsafe or harmful outputs, bias, privacy leakage, prompt injection, tool misuse, unsupported claims, data exposure, out-of-distribution inputs, or failures in dependent services. The relevant tests depend on the system’s use and applicable domain requirements; a generic checklist cannot cover every hazard.
Assess not only whether a failure occurs, but also what the application does next. Check whether it limits the impact, communicates uncertainty appropriately, avoids unsafe actions, and routes the case to a safe fallback or human review when needed. NIST’s AI RMF calls for evaluation of safety risks, security, and resilience, and for systems to fail safely within the relevant risk tolerance. Possible controls include monitoring, human intervention, and the ability to modify or shut down a system.
For generative AI, NIST’s Generative AI Profile calls for empirically validated capability evaluation, deployment-like performance or assurance criteria, and communication of pre-deployment test results to release approval authorities. NIST’s ARIA program describes model testing, red-teaming, and field testing as evaluation levels; these are useful categories for planning, not a requirement that every application participate in the program.
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How should teams compare candidates and make a release decision?
Use a side-by-side scorecard, but do not collapse unlike risks and outcomes into one unexplained number. A comparison should show the measures that matter to the intended use, the evidence behind them, and any tradeoffs the release approver must accept.
| Evaluation axis | What to compare | Useful reporting |
|---|---|---|
| Task quality | Task completion, correctness, omissions, grounding, consistency, and appropriate abstention for the use case | Overall and slice-level results; scoring method and uncertainty |
| Latency | User-visible response time and tail behavior under representative load | P50 and P95 or other relevant percentiles; time to first token and total duration where applicable; errors and timeouts |
| Cost | Total cost of delivering a successful task at realistic volume | Cost per successful task; token, service, retry, and tool assumptions |
| Safety and security | Mapped harms, robustness, privacy and security risks, and failure handling | Scenario results, residual risks, fallback, and escalation readiness |
| Operational readiness | Monitoring, incident response, rollback, change management, and ownership | Named approvers, alert conditions, response playbooks, and review cadence |
Before testing, document which outcomes are mandatory gates and which are tradeoffs. A strong average quality score should not obscure a serious failure in a high-impact slice, and a favorable cost or latency result should not compensate for failure to meet a required safety bar. There is no single set of weights or thresholds that fits every deployment.
What belongs in an AI launch-readiness record?
Give approvers a concise, reviewable record of the evidence and the decision. It should include:
- Intended use, out-of-scope uses, users, deployment conditions, and mapped impacts.
- Test set and method, system versions, acceptance criteria, and results by important slice.
- Quality, latency, cost, safety, security, and reliability findings, with uncertainty and limitations.
- Residual risks and the rationale for accepting, reducing, or avoiding them.
- Release decision, accountable approver, and any conditions or limits on rollout.
- Monitoring signals, alert ownership, fallback or rollback path, and incident response process.
- User feedback, escalation, appeal or override routes where applicable, and a review cadence.
NIST’s AI RMF calls for documented risk and impact information, objective and repeatable testing, and post-deployment monitoring, incident response, recovery, and change management. Evaluation should continue after launch rather than treating the release result as permanent assurance.
When should you run the evaluation again?
Re-evaluate when a material part of the system or its operating conditions changes. Examples include a model update, prompt or policy change, retrieval-corpus revision, new tool, changed data, or a different deployment environment. Also use production monitoring, incidents, and user feedback to identify cases missing from the test set, then add them to later evaluation cycles.
NIST’s framework calls for testing before deployment and regularly during operation. Its AI RMF 1.0 page says the framework is being revised, so teams using it should check NIST’s current framework materials when setting their governance process. OpenAI’s Working with evals documentation describes a vendor-specific platform transition: it lists October 31, 2026 for read-only access for existing users and November 30, 2026 for scheduled shutdown of the Evals platform, and recommends Datasets for a new iterative environment. These dates concern that platform, not the evaluation method itself; confirm current availability with OpenAI before relying on the tooling.
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