Define Success Metrics for an AI Coding Assistant

What interviewers evaluate

• Do you anchor on the product's actual goal (developer output and quality), not AI activity?
• Do you spot why the obvious metric (acceptance rate) is gameable and insufficient?
• Do you include quality guardrails - the failure mode specific to AI products?
• Do you separate leading indicators from lagging business outcomes?
• Do you propose a causal measurement (holdout), not just correlational dashboards?

A framework to structure your answer

1. Goal - why does the product exist? Ship correct code faster; success = developer productivity and quality, not model activity.

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