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Keep a versioned set of realistic test cases, run the old and revised prompts against the same cases, and compare results case by case. Check both the behavior you intended to fix and important behaviors that already worked. A higher average score is not enough if the target failure persists or a consequential regression appears.
What to save before editing the prompt
Save the current prompt and the conditions needed to interpret its results: model and runtime configuration, relevant tool or data settings, the evaluation cases, and the criteria used to judge them. This record helps separate the effect of a prompt edit from changes to the model, tools, data, or runtime.
Keep the evaluation cases stable enough to compare runs over time. Google Cloud describes test-case evaluation as regression testing against a specific dataset in its agent evaluation guidance.
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Build a test set that checks both the fix and its side effects
Start with the real example that motivated the change, then add cases that test nearby variations and representative examples the previous prompt handled correctly. The goal is not to maximize the number of tests; it is to cover the behaviors a broad prompt edit might affect. There is no universal sample recipe established by the cited guidance, so select cases based on the agent’s actual use and the consequences of failure.
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- Keep the triggering failure: Verify that the revised prompt handles the original problem, not merely a cleaned-up version of it.
- Add variations: Change relevant wording, context, or user intent to check whether the fix generalizes without overfitting to one example.
- Preserve successful cases: Include high-value examples that test existing behavior the edit could damage.
- Use production-representative inputs: Google Cloud’s evaluation dataset guidance recommends prompts representative of inputs processed in production. The page was marked Preview when reviewed, so its details and status may change.
For a multi-turn agent, include the relevant conversation history. For a tool-using agent, capture the intermediate events that matter, such as function calls, function responses, and intermediate model responses. A final answer alone may look acceptable even when the revised prompt changed which tool the agent chose or how it used the result.
Choose checks that match the behavior
Define how each case will be judged before comparing prompts. A grader can only tell you what its criteria measure; its availability does not establish that it reliably captures the quality you care about.
| What you need to evaluate | Suitable check | What to watch for |
|---|---|---|
| Exact output requirements, such as required fields or prohibited strings | Deterministic string or code-based checks | Passing the format check does not prove the answer is useful or correct. |
| Similarity to an expected answer | A similarity grader | Similar wording can still miss a material distinction; inspect cases where that distinction matters. |
| Broader qualities, such as instruction adherence or task success | A defined rubric, potentially evaluated with a model-based grader | Review uncertain or high-impact judgments rather than treating every score as ground truth. |
| Ambiguous, safety-critical, or consequential outcomes | Human review alongside automated checks | Specify what reviewers should assess so judgments remain tied to the intended behavior. |
The Graders API reference documents string, similarity, Python, score-model, label-model, and combined grader forms. Those are available mechanisms, not proof that a particular grader configuration is accurate for your task. The Evals API reference documents evaluation runs.
Compare the old and revised prompts on identical cases
- Fix the comparison conditions. Use the same evaluation cases and, where possible, the same model, settings, tools, and runtime for both prompt versions. Record unavoidable differences.
- Run both versions. Evaluate the saved baseline and revised prompt using the same criteria.
- Inspect per-case outcomes. Compare the original failure, variations, and protected success cases individually, not only the aggregate result.
- Review agent traces when relevant. For multi-turn or tool-using workflows, inspect conversation history and intermediate events as well as final responses.
- Group recurring failures. Look for patterns that explain whether the intended behavior improved and whether a new failure appeared. Google Cloud’s agent workflow includes failure analysis and clustering as part of refining instructions or tool descriptions.
Google Cloud distinguishes rapid evaluation for development changes, dataset-based test-case regression, and continuous online monitoring in its agent evaluation guidance. Choose the stage that fits the question: a quick development check can help while editing, while the saved dataset provides a consistent regression comparison.
Set an acceptance rule before release
Decide what must improve and what must not regress before looking at the results. For example, require the motivating behavior to pass while critical safety or business checks remain within your chosen limits. The appropriate threshold depends on the task and its risks; the cited sources do not establish a universal score, minimum dataset size, or statistical confidence threshold.
If a new failure appears, inspect the example and its trace, then check whether the prompt edit plausibly caused it or whether another component changed. A blended score can conceal a severe regression in a small but important case group, so use the per-case evidence to make the release decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Continue evaluating after deployment
An offline test set cannot cover every live input. After release, monitor deployed behavior and feed meaningful new failures or representative successes into later regression runs. Google Cloud describes online monitoring as continuous evaluation of a production agent in its agent evaluation guidance.
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