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To prevent LLM features from quietly regressing, test the complete feature—not just the model—against documented, representative cases before release, then keep measuring it in production. Treat prompts, retrieval data, tools, orchestration, safeguards, and the user-facing environment as parts of the system; compare changes with a known baseline and feed confirmed failures back into the test suite.
Start by defining what the test covers
An LLM application’s behavior can change even when its model stays the same. A prompt edit, new retrieval corpus, changed tool, different retry policy, or safeguard update may alter what a user receives. Conversely, a model-only benchmark cannot establish how the complete product behaves in its deployment context.
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Before evaluating a change, map the components that can affect the outcome: model and settings, prompts or task definitions, retrieval sources, tools, orchestration, filters, and the interface or environment in which users act on the result. For agents and multi-step workflows, include the harness, scaffolding, retry rules, and resource budget. OpenAI’s guidance on third-party evaluations emphasizes that these conditions affect what a result can substantiate; a score from one setup does not automatically transfer to another. OpenAI’s evaluation playbook
Define the boundaries too: intended users and tasks, dependencies (including third-party data or software), and the consequences of a wrong or incomplete answer. NIST’s AI Risk Management Framework says context and impact mapping should inform measurement and go/no-go decisions. NIST AI RMF: Measure
Turn the product promise into observable criteria
Write down what a successful result looks like in terms a reviewer or test can check. “Answer questions well” is too vague to guide a release decision. Specify, for example, what makes an answer correct, incomplete, unsafe, unsupported, or a task failure for this feature. Include important boundaries such as when it should decline, request clarification, or avoid taking an action.
For an agent, define both the desired task outcome and the conditions under which it must reach it: available tools, permissions, retry behavior, and limits on time or other resources. This makes it possible to distinguish a failure in the model’s response from a failure in tool access or workflow design.
Map criteria to the risks that matter for the feature. A user-facing assistant that summarizes documents may need checks for whether claims are supported and whether important context is omitted. A workflow that invokes tools may also need checks for correct tool selection, permissions, and completion of the requested action. There is no universal checklist or scoring recipe: choose measures for the feature’s actual promise and potential harms.
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Build an evaluation set that can be rerun
Keep a stable regression core so a future run can be compared with a previous one. Assemble it from product requirements, representative user tasks, boundary cases, known incidents, and the risks identified for the feature. Record where the cases and reference material came from, how they were prepared, and where they may not represent real use.
A useful set is not just a handful of easy examples. Include varied inputs and situations that matter in deployment, such as ambiguous requests, missing information, unusual but valid inputs, and cases where the right behavior is to refuse or hand off. The exact mix depends on the product; the aim is to expose meaningful failure modes, not to claim that a finite set covers every possible interaction.
NIST recommends documenting test sets, metrics, and tools, and warns against extrapolating performance from narrow, anecdotal assessments. Use a set whose limits are visible, and state what kinds of users, tasks, and conditions it does—and does not—represent. NIST’s Generative AI Profile
Use different evaluation methods for different kinds of behavior:
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- Reference-based or rubric-based review for semantic qualities such as relevance, completeness, or whether an answer follows the product’s requirements.
- Human review where ambiguity, impact, or the stakes of a decision make automated scoring insufficient.
This is a practical mix, not a mandated formula. NIST supports quantitative, qualitative, or mixed measurement, rather than prescribing one scoring method for all LLM features. NIST AI RMF: Measure
Measure the behavior that matters, not just an average
Choose measures that correspond to the acceptance criteria and mapped risks. Depending on the feature, those may include task completion, categories of error, grounding and citation support, safety behavior, tool or workflow completion, latency, or cost. Track the conditions under which each result was obtained, and compare the new run with a baseline collected under comparable conditions.
Keep distinct failure types visible. An overall success rate can conceal a serious safety failure or a regression concentrated in one important class of requests. Look at results by relevant case category and investigate meaningful failures rather than allowing strong performance in one area to cancel out a critical weakness in another.
Report uncertainty and limits alongside results. NIST calls for rigorous testing, measures of uncertainty, comparisons with benchmarks, and formal reporting. Its guidance does not set a universal pass percentage or test-set size for LLM applications. A team must decide what evidence is adequate for its own feature and risk, and document risks it cannot yet measure. NIST AI RMF: Measure
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Rank #4
Test grounding and citations as evidence, not decoration
If the feature retrieves documents or presents sourced answers, assess whether the cited material actually supports the claims. Also check whether the answer omits important context or makes a claim stronger than the available evidence warrants. A citation being present is not itself proof that the answer is grounded.
NIST’s work on evaluation probes for agentic AI separates these checks into three dimensions: faithfulness (does the source support the claim?), completeness (does the answer capture the source’s full message?), and sufficiency (is the evidence adequate for the claim being made?). Its proposed approach connects outputs to evidence in a structured audit trail. NIST: Building Evaluation Probes into Agentic AI
Apply these checks when they fit the feature’s promises; do not treat them as a general score for every kind of LLM response. NIST’s Generative AI Profile also recommends reviewing and verifying sources and citations during pre-deployment measurement and ongoing monitoring. NIST’s Generative AI Profile
Use release gates that reflect the feature’s risk
Run the documented suite when changing any component that can affect behavior: model, prompt, retrieval data, tool, workflow, or safeguard. Keep task cases and evaluation conditions comparable when comparing versions. If the harness intentionally changes, record that difference; results from unlike setups may not support a direct comparison.
Best Value
Set acceptable thresholds and escalation rules for the feature rather than borrowing a universal LLM pass mark. A release decision should consider relevant quality and risk measures, not just one average score. For a high-impact failure mode, define whether any occurrence blocks release, requires human investigation, or triggers another safeguard; for less consequential variation, a team may set a different response. Make the rationale explicit.
If a risk cannot be measured reliably, record that limitation and account for it in the decision instead of treating missing evidence as a passing result. NIST recommends using measurement to inform risk-management decisions, but leaves the choice of appropriate methods and metrics to the organization. NIST AI RMF: Measure
Keep the production feedback loop active
Pre-release evaluation is not a substitute for operational monitoring. After deployment, monitor both system components and user-visible behavior, track errors and emerging risks, and provide routes for users or affected communities to report problems. NIST’s AI RMF calls for testing before deployment and regularly during operation, with ongoing attention to feedback and changing risk. NIST AI RMF: Measure
When a production issue is confirmed, investigate which condition failed and whether the system’s use has changed. Turn the incident into a regression case when it can be represented responsibly, then update the relevant acceptance criteria or response process. Reassess whether the evaluation set still reflects actual use and whether earlier assumptions about safety or grounding remain valid. This closes the loop: production observations improve the next test run, rather than remaining isolated incident reports. NIST’s Generative AI Profile
Record enough for another person to reproduce the decision
For every run, preserve a concise evaluation record. It should identify:
- The model and relevant settings, plus the prompt or task definition tested.
- The evaluation-set and retrieval-data versions, and the provenance or known representativeness limits of the cases.
- The tools, harness, scaffolding, safeguards, and evaluation conditions.
- The scoring method, results by relevant category, baseline comparison, and uncertainty or limitations.
- The release decision and its rationale, including unresolved risks or follow-up actions.
For agentic evaluations, also record attempts, retries, time, and token or cost budget where relevant, along with how validity threats were checked. A report should let a future reviewer tell what system was evaluated, under what conditions, and how far the conclusion can reasonably reach. NIST calls for formalized reporting and documentation; OpenAI’s third-party evaluation guidance adds detail on reporting agentic task distributions, setups, budgets, and validity checks. NIST AI RMF: Measure · OpenAI’s evaluation playbook
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