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Every completed Improve cycle pauses for human or external AI review. Use the review page to decide whether Adaline’s candidate should be approved, edited, or rejected. The reviewer, whether a person or external AI agent, owns the production decision: the diagnosis must match the real issue, the diff must be understandable, impact must be acceptable, and the deployment target must be correct. Improve review page showing diagnosis, representative failing traces, prompt diff, and traffic comparison

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Read the review page top to bottom

Start with the evidence path. A good review package shows how the cycle moved from production evidence to candidate prompt changes, and whether each stage produced enough signal to support a release decision. Improve cycle stage provenance showing Behaviors, Evals, Datasets, Prompts, and Review evidence Then move through the candidate review itself. Read the diagnosis first, confirm the selected candidate, inspect the diff, compare example outputs, check regressions, and only then look at deployment impact. Candidate review traffic comparison showing current and improved outputs for tested conversations Improve regression report and runtime tradeoff section showing evaluator scores, cost, latency, and token movement Before approving, make sure the diagnosis matches the customer or product problem, the supporting Behaviors and logs are relevant, the prompt diff is understandable, evaluator or dataset regressions are acceptable, runtime impact is acceptable, and the deployment target is the one you intend to change.
Approve can affect production when the project has a deployment environment. Use Edit & approve when you want to inspect or adjust the prompt before deployment.

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After the decision

Improve review action bar showing no regressions, target prompt, cycle number, version creation, reject, and edit and approve actions You can also export the audit packet as JSON for records, external AI review, or a no-human-in-the-loop handoff before deciding what happens next.

Export Audit Packet

Download the review evidence as JSON for records or external systems.

Auto Prompt Optimization

Understand candidate exploration, safety gates, and scoring evidence.

Auto Generated Evaluators

See how generated evaluators help check the candidate before release.

Deploy your prompt

Continue from reviewed prompt version to deployment.