How does AI get better? Why your team's corrections matter
By Rodney Noriega, Founder, Universal Enablement · Published Oct 12, 2026
Short answer
AI learns from feedback. Choices that lead to good results become more likely, and choices that lead to bad ones become less likely. Nobody programs the strategy. The assistants you use today were trained the same basic way, at enormous scale. In your own workflow, your team's corrections are the feedback that makes results better.
In 1961, British researcher Donald Michie built a learning machine out of 304 matchboxes. Each box stood for one tic-tac-toe position and held coloured beads, one colour per possible move. To play, you pulled a random bead from the box for the current position.
After each game, winning moves got extra beads. Losing moves had beads removed. Nobody told the boxes that corners are good. After a few hundred games, the machine rarely lost.
Three lessons for your business:
- Feedback shapes the result. What you reward and correct is what improves.
- Learning can go wrong. A good move can get punished because of a bad move later in the same game.
- Track the fixes. If reviewers keep correcting the same thing, change the instructions.
You won't retrain ChatGPT or Claude yourself. But you will tune the instructions, examples and checks around them. That's where your team's feedback goes.
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