Build feedback loops that compare AI predictions against actual outcomes to improve over time
Track whether AI predictions matched reality by logging actual outcomes in a feedback column. Review patterns weekly and update prompts based on misclassifications. Version prompts to separate accuracy improvements from data mix changes. This teaches the system what 'good' looks like.
When to use
For any AI system making predictions or recommendations. Essential when you need the system to improve rather than stay static.
Don't do this
Deploying AI and never checking if predictions were correct. Running AI without tracking outcomes means you can't improve and don't know if it's working.
1 Founder Who Did This
Added Feedback column to track if leads actually converted after AI scored them. Reviewed patterns weekly to find misclassifications (e.g., AI scoring freelancers as cold but they kept converting). Updated prompts based on patterns and versioned them to measure true improvements.