Walk Through an AI-Assisted Project

Talk about a project that involved AI: the issue it solved, what you personally did, how the results were verified, and the places where you intentionally kept the model out of the loop.

Constraints & Assumptions

• Base it on an actual project, but leave out confidential information and proprietary prompts.
• Distinguish what the model itself could do from the product and engineering work around it.
• Don't assert quality improvements unless you can explain how they were measured.

Clarifying Questions to Ask

• Which user or operational problem made AI the right choice?
• What evaluation set or review process was used to check output quality?
• Which failure cases needed guardrails or a human in the loop?

Hint — Mark the boundary: Be explicit about which decisions the model suggested and which were controlled by the system or a person.

What a Strong Answer Covers

• How you chose the problem and why an AI component fit it.
• What you did across data, prompting or training, integration, evaluation, and safeguards.
• Specific failure modes, fallback behavior, and privacy or cost tradeoffs.
• A quantified outcome and an honest description of the limits that remain.

Follow-up Questions

1. What would lead you to take the AI component out?
2. How would you catch quality drift after launch?

Overview: Construct an interview response about an AI-assisted project that emphasizes evaluation, guardrails, ownership, and limitations rather than hype.