Practical AI
How to Evaluate an AI Feature Before Building It
Practical AI begins with a measurable workflow outcome, suitable data, explicit review boundaries, and acceptable failure modes.
Define value without mentioning a model
Describe the user decision, delay, error, or manual effort the feature should improve. If the value proposition only works when described as “adding AI,” the product case is not yet clear.
Evaluate the operating constraints
Review data availability, privacy, accuracy tolerance, latency, cost, model dependency, security, and the people responsible for reviewing uncertain output. The acceptable design for drafting text is different from the design for making a consequential decision.
Make evaluation part of the product
Define representative test cases, failure categories, quality thresholds, feedback capture, and fallback behavior before launch. Monitor usefulness and failure patterns in production so the feature can be improved or retired based on evidence.