Set up continuous evaluation by creating a representative test set, defining observable success criteria, choosing a suitable grader for each criterion, and saving a baseline. Rerun the suite when the model, prompt, tools, or application behavior changes; after launch, evaluate an appropriate sample of production outputs, inspect failures, and update the test set. Before using production records, confirm that the evaluation service’s privacy, retention, and access controls fit your data.
What continuous evaluation means
An evaluation pairs examples with criteria and grading logic. In practical terms, give the AI application an input, then assess its output against a defined measure of success. The cases, criteria, and graders should be explicit so the evaluation remains understandable even if you change tools or frameworks. See the OpenAI Evals API reference and Anthropic’s evaluation guidance.
Continuous evaluation extends testing beyond a pre-release check. You evaluate the application repeatedly as it changes and, once deployed, use suitable production outputs, user feedback, and ground truth where available to monitor performance over time. Google Cloud’s production evaluation guidance describes this ongoing loop.
Set up the evaluation in six steps
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Define observable success criteria
Translate the application’s intended task into criteria a reviewer or grader can assess. Depending on the task, these might include factual correctness, required format, policy adherence, or successful tool use. Keep distinct failure types separate when they require different fixes; an undifferentiated quality score can hide what went wrong. The right criteria come from your application’s requirements, not from a universal checklist. OpenAI supports configurable evaluation criteria, while Google Cloud describes evaluation metrics for generative AI applications.
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Build a representative dataset
Include routine inputs, important edge cases, and examples of known failures. For each case, store the relevant input and, if available, a reference answer, label, rubric, or other ground truth. Human assessment can supply ground truth; Google Cloud also describes using an ensemble of AI systems to generate evaluation metrics. Treat automated judgments as hypotheses to validate, not unquestionable labels.
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Match each criterion to a grader
Use a deterministic check when the requirement is mechanical, such as a required string or format. OpenAI documents string-check, text-similarity, Python, and model-based score or label graders. These methods are not interchangeable guarantees of correctness. Review representative examples and compare grader decisions with human-reviewed cases, especially for subjective criteria.
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Save a baseline and test changes
Keep evaluation cases and configuration stable enough that results can be compared across runs. Record a baseline, then rerun the suite when changing the model or its parameters, prompts, tools, or application behavior. Use the results to identify regressions before rollout. The OpenAI Evals API supports running evaluation criteria against models and parameters.
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Extend evaluation into production
Capture a suitable sample of production outputs and evaluate it on an ongoing schedule or with an online monitor. Where available and appropriate, incorporate user feedback and compare outputs with ground truth. This lets you track how performance in production differs from development results. Google Cloud describes production evaluation in its deployment guidance and configured metrics and logs for ongoing quality assessment in its online monitoring documentation.
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Investigate failures and refresh the suite
Review failed examples, application transcripts, and grader decisions. A low score may indicate a genuine application error, but it may also mean the grader rejected a valid response. Add meaningful new failure cases as usage changes. Watch for saturation: if every capable version passes a case, it may still catch regressions but provide little evidence of further improvement. Anthropic’s evaluation guidance emphasizes examining examples and grader judgments.
Choose evaluation tools by workflow fit
No single vendor is established as the best choice for every application. Compare services against the requirements of your own evaluation loop:
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- Data and run management: Can you store examples, reference labels, and metadata, then rerun them against relevant model or application versions? The OpenAI Evals API reference documents its evaluation-run model.
- Grader options: Does the service support the deterministic, code-based, similarity, rubric-based, or model-based grading your criteria need? See OpenAI’s documented grader types.
- Production monitoring: Can the service evaluate the outputs or traces in your production architecture and expose enough detail to investigate results? Google Cloud describes configured production metrics in its online evaluation documentation.
- Data handling: Check retention, privacy, and access controls before sending production records to an evaluation service. OpenAI’s data controls documentation lists
/v1/evalsapplication state as retained until deleted and says the endpoint is not eligible for Zero Data Retention. Verify current provider and organization settings before using production data. - Debuggability and maintenance: Make sure people can inspect failed cases, transcripts, and grader outputs, and that the team can refresh the dataset when real-world usage changes. See the operational guidance from Anthropic and Google Cloud.
What to monitor over time
Track the individual criteria that matter to the application rather than relying only on an overall score. Compare each run with the saved baseline, inspect notable changes, and use production feedback or ground truth to identify gaps in the test set. Preserve enough case-level detail to distinguish a model regression from a grader mistake or a shift in the kinds of inputs users provide.
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