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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Is your LLM quietly getting worse? A small, repeatable evaluation loop can help you spot changes in the quality of an AI feature and find examples to investigate. It cannot, by itself, prove that a model has degraded: outputs vary, inputs change, and a small test set is not statistical certainty.
If you’re asking, “How do I monitor LLM quality in production?” or “How can I detect LLM drift?”, start by testing the task users actually rely on. Save a relevant set of cases, compare results with a baseline, and treat a score shift as a reason to inspect—not as a diagnosis.
What a tiny LLM drift detector can—and cannot—tell you
An evaluation is a repeatable task definition: it pairs a data source with criteria or graders. For an AI feature, those criteria should represent user-facing quality, not just infrastructure health. A service can respond quickly and successfully while still giving unsupported or incomplete answers.
A compact set of representative examples can make a change visible and give you concrete failures to review. Its aggregate score is a signal about those cases, not proof that all production traffic has changed or that the model caused the difference. Outputs can vary, and the conditions around a request can change too.
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OpenAI’s evaluation documentation describes evaluations in terms of data sources and criteria, including comparisons across models and parameters. Its backward-compatibility guidance notes that behavior can change between model snapshots and recommends pinned versions and evaluations for more interpretable comparisons.
Build a small, useful evaluation loop
- Define the failure you want to catch. For example, a support assistant might answer with unsupported claims. Choose one or two observable criteria tied to that user-visible failure, rather than trying to measure “quality” in the abstract.
- Assemble a compact set of cases. Use representative real examples where appropriate, or carefully constructed cases. Give each an expected result for exact tasks or a grading rubric for subjective ones. Save case identifiers and version the cases with the prompt and model configuration.
- Choose a grader that fits the criterion. Use deterministic code for constraints that can be checked exactly, such as required fields or valid formats. Use a rubric with human review, or a model-based grader, for qualities that need judgment. OpenAI documents multiple grader types; Arize Phoenix describes both code-based and LLM-as-judge approaches.
- Run a baseline and record its context. Run the feature and evaluator on the saved set before changes, after prompt, model, or retrieval changes, and periodically if that helps your team. Record the score, case identifier, time, model or snapshot, prompt version, and relevant configuration. Without this context, a score comparison may conceal what actually changed.
- Compare results at both levels. Look at the aggregate result and which individual cases passed or failed. A stable overall score can hide one important regression; a changed score can reflect a few cases or ordinary output variability.
- Investigate before assigning cause. When results shift, inspect examples and check whether the inputs, prompt, retrieval behavior, model configuration, or surrounding application changed. The model snapshot is one possible factor, not an automatic explanation.
- Update the set thoughtfully. Add confirmed, representative failures so future runs can catch them. Keep a human review path for important or subjective judgments, and periodically check that an LLM judge agrees adequately with human labels for your use case.
Pick checks that match the feature
| Check type | Works well for | Trade-off |
|---|---|---|
| Deterministic code | Exact constraints such as required fields, permitted values, or format rules. | Easy to explain when a check fails, but it cannot establish subjective qualities that are not encoded in the rule. |
| Rubric with human review | Important or nuanced judgments where a person needs to assess the response against explicit criteria. | Provides direct human judgment, but requires reviewer time and consistent rubrics. |
| LLM-as-judge | Rubric-based assessment at a scale or cadence where manual review alone is impractical. | Its judgments need validation against human labels; it is not an unquestionable ground truth. |
These approaches can be combined: use code for exact constraints, then route a sample or higher-risk cases for human review. Phoenix’s evaluator documentation covers code-based and LLM-judge evaluators alongside traces and experiments.
Set a local review trigger, not a universal threshold
There is no source-supported alert percentage or sample size that works for every feature. A trigger should reflect the task’s risk, the variability you observe across baseline runs, and how many examples your team can review. A small set is useful for an early warning and diagnosis, but it does not establish statistical certainty.
When a trigger fires, treat it as a request to review cases. Check for a genuine quality change, ordinary variation, changed workload, or changed configuration before deciding whether to roll back or adjust the feature. NIST’s March 6, 2026 publication, Challenges to the monitoring of deployed AI systems, describes validated post-deployment monitoring methods as nascent and scattered.
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Start offline; add production traces only when useful
A local regression run or provider evaluation can be enough for a first detector. Running the same saved cases after a prompt or model change is simpler to interpret and avoids building a production alerting system before you know which failures matter.
If you need to understand behavior on real traffic, production traces can provide examples to evaluate. That adds operational and privacy responsibilities: traces may include prompts, answers, and metadata. Keep the retained data limited and protected, and decide who can access it and how long it is needed.
Arize Phoenix documents traces, datasets, experiments, and evaluation workflows; its documentation points to Arize AX Online Evals for production monitoring with alerting and threshold triggers. That is an optional fuller workflow, not a requirement for a small detector. Verify current product capabilities and account terms before adopting a platform.
For teams formalizing the practice, the NIST AI Risk Management Framework Core calls for documented, repeatable or scalable testing, evaluation, verification, and validation, as well as monitoring system behavior and functionality in production.
Protect evaluation and trace data
Evaluation cases and production traces can contain sensitive user content. Minimize what you retain, protect access, and review the controls for the specific provider, endpoint, and account you use. Do not assume one vendor’s retention policy applies to another.
OpenAI’s data controls documentation says API data is not used to train or improve OpenAI models unless a customer opts in. It also describes default abuse-monitoring retention of up to 30 days and endpoint-dependent application-state retention and eligibility for controls. These are OpenAI-specific statements; check the current policy and your endpoint and account settings before using them to design a logging workflow.
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