For the Claude Corps exam, I worked on a scenario involving Riverbend Food Alliance’s shared email inbox. The goal was to create a prompt that could classify emails, identify urgent messages, route them to the correct staff member, and generate a safe draft response. The biggest thing I learned was that a good AI workflow is not only about getting the classification correct. The draft itself can create serious problems if the model invents information. In the original failure examples, Claude included unsupported details such as business hours, attachments, response times, intake procedures, delivery options, and allocation approvals. Even though the responses sounded helpful, they could cause confusion or damage relationships with donors and partner organizations. I revised the prompt by creating exact category and routing rules, clearer urgency standards, and specific instructions telling Claude not to invent operational information. I also kept a human in the loop for decisions involving inventory, allocations, logistics, policies, or commitments. The model’s role was to organize the inbox and prepare a safe acknowledgment, not make decisions for the organization. One judgment call I had to make involved partner allocation requests. Even when an email was addressed to Marcus, the exam’s routing rules required those requests to go to Diane. I also decided that a future date alone should not make an email urgent. There needed to be an actual short deadline, risk of food loss, or immediate disruption. One thing I would do differently is define the evaluation labels before finalizing the prompt. I noticed late that the ground-truth file used “media_inquiry,” while the required prompt label was “media_or_general_inquiry.” That mismatch could cause a correct answer to be marked wrong. Overall, this project showed me that reliable AI systems need clear rules, structured testing, and human review. A confident response is not useful if it contains information the organization never approved.

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