Errors & Feedback

How AI learns when it's right and when it's wrong

Errors and feedback are how AI systems improve over time, and a system with no way to catch mistakes will keep making them.

AI is not right by default. It is a probabilistic system that produces plausible output, and plausible is not the same as correct. Errors happen, and without a system for catching and learning from them, the same errors will happen again. The goal is not zero errors but a loop that finds them, learns from them, and reduces them over time.

Feedback comes in different forms. Some is direct: a reviewer marks an output as wrong and explains why. Some is indirect: a rate of correction, a pattern of user edits, a downstream result that does not match what the output predicted. Both kinds are useful, but indirect feedback only works if someone is looking for it.

Designing feedback in from the start is easier than retrofitting it later. Every AI process should have an answer to these questions: how do we know when the output is wrong, who sees that signal, and what do we do about it?

Make it visibleSet up a simple shared log (a spreadsheet or shared note) where anyone on the team can record an AI error: what the output was, why it was wrong, and what the correct version should be. Review it monthly and update the relevant prompts.

Why AI needs this

Each part of your work matters to AI in a specific way. Some of it is context a tool needs before it can help, some of it is work a tool can take on, and some of it is judgment that should stay with you.

Catching errors before they compound

A single bad output is a small problem. A process that keeps producing the same error at scale is a large one. Early detection limits the damage.

Signal that the system is drifting

AI performance can degrade as context changes, tasks evolve, or the model is updated. Feedback is how you notice the drift before it becomes a problem.

Improving the prompt and context

Most errors trace back to unclear prompts or missing context. A feedback loop gives you the evidence to fix the right thing rather than guessing.

Accountability for AI output

When there is a process for reviewing and reporting errors, someone owns the quality. Without that process, errors are everyone's problem and no one's responsibility.

Questions to explore

Use these on your own or in a group. There are no right answers, only better conversations.

For each AI task your team runs, how would you know within a day if the output was wrong?
What is your current process when someone notices AI has made an error?
Who has the authority to change an AI process when recurring errors are identified?
Where does feedback about AI quality currently go, and does it result in any changes?
What types of errors would be hardest to catch in your current AI use, and why?

Readiness traps

Assuming low error rates mean no errors. Probabilistic systems produce errors at a rate, and that rate matters more as the volume of output increases. Measure it rather than assuming.
Feedback that goes nowhere. Collecting error reports or corrections that no one acts on creates the appearance of accountability without the substance. The loop is only closed when the system changes.
Catching errors at the output but not tracing them to the cause. Fixing a single wrong output is less valuable than finding the prompt or context gap that produced it and closing that gap.