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AI operations · Velozity perspectives

Turn AI feedback into an improvement process

Corrections are valuable when they become evidence for a deliberate change.

AI operations
Ideas into capability.

Capture the reason, not just the reaction

A thumbs-down signal says that something went wrong but may not explain what. Where practical, distinguish missing evidence, incorrect extraction, an unsuitable action, and a confusing experience. Let reviewers record the corrected result or the missing information. Collect only the data needed for the improvement process.

Look for repeated patterns

A single error may require immediate correction. A repeated error suggests a gap in the system. Group feedback by workflow step, source, and failure type. This can help distinguish a documentation problem from a retrieval issue or a rule that needs clarification. The intervention should match the cause.

Test the proposed change

An improvement might involve a better source document, a revised prompt, a validation rule, or a different model. Compare the proposed behavior on representative examples, including cases that previously worked. Keep a record of what changed and why. Feedback should inform a decision; it does not have to trigger an automatic production update.

Close the loop with the team

Make reviewed changes and unresolved gaps visible to the people contributing feedback. Monitor the next release for the issue you intended to address. Continue checking that improvements hold across the wider workflow. A sustainable process gives feedback an owner, a review rhythm, and a clear route into delivery.

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