Understand your AI’s customer experience
Review conversations, capture feedback and test prompt changes with Lunary.
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Give engineering a clear example of what went wrong and what a useful answer should look like.
Review
Read the conversation and the result the customer received.
Explain
Record what was wrong, incomplete or unhelpful.
Improve
Review prompt changes against real customer examples.
At a glance
| Product question | Useful evidence |
|---|---|
| Are people getting help? | Task outcomes plus reviewed conversations |
| What should we improve? | Repeated failure patterns and their severity |
| Did the prompt change help? | Comparable examples before release and outcomes after |
| Is usage healthy? | Adoption alongside completion, abandonment, and feedback |
| What should engineering do next? | A specific example and acceptable behavior |
Start with the conversation
Lunary brings chats, threads, user context and feedback into the review. Look for repeated issues across successful and unsuccessful tasks.
Make feedback actionable
Share the conversation or run, the customer’s goal, the actual result and the expected behavior. Engineering can use the linked execution context to find the cause.
Review changes together
Use prompt templates and the playground to inspect improvements and regressions. After release, track task completion and helpfulness alongside usage.
Questions & answers
Do reviewers need to understand every trace?
No. Explain the customer experience and expected behavior; engineering can investigate the related runs.
Is thumbs-up feedback enough?
It is useful but incomplete. Combine feedback with task outcomes and representative conversation reviews.
By Lunary · Documentation reviewed Sep 16, 2026
Sources & methodology (5)
Based on vendor documentation, not an independent benchmark or hands-on rating. Features and plans can change.
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