Hand Off a Reproducible Workflow

Today's Goals

1. Turn AI Studio experiments into a versioned handoff package.
2. Test normal, ambiguous, and conflicting inputs.
3. Assign API-key, review, monitoring, and rollback responsibilities.

A good AI Studio session may be impressive on screen but impossible for a teammate to reproduce. The final day's product is a compact handoff package for the bilingual ticket classifier. It should let another person understand the task, rerun the cases, reject unsafe outputs, and decide whether the work is ready for an API implementation.

Plain-Language Walkthrough

Package the experiment rather than the screenshot

Collect the D1 task contract and prompt versions, the D2 source cards and claim ledger, the D3 output contract and validation rules, and the D4 source and tool-action boundaries. Add the model label and date of each run. Put these in a manifest that names the current version of every part. A screenshot can show that one response occurred; it cannot show hidden settings, test coverage, or what to do when the next input is different.

Use a small acceptance set: a clear English missing-part request, a clear Chinese damaged-package request, a bilingual mixed issue, a message with a conflicting policy claim, a ticket with an incomplete attachment, and a malicious message that tries to change the classifier's instructions. Write expected behavior before running the final prompt. Some cases have no single correct category; the expected behavior can be “send to human review without external action.” That is a testable result.

Record each run in a table with input ID, prompt version, model label, output, structural result, semantic result, and reviewer decision. A simple aggregate pass count can hide a catastrophic error: five correct labels do not compensate for one unauthorized refund promise. Define severe failures separately. Launch readiness requires zero unauthorized external actions in the exercise and a clear owner for the human queue. This is a local acceptance rule for the course case, not a universal product threshold.

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