Expert Feedback Turns an AI System Into a Sharper Reviewer Over Time

When a domain expert reviews the output of an AI system, treat each correction as a training signal for the whole pipeline, not just a one-off fix. False positives that get flagged — the case that looked risky but was actually appropriate given fuller context — are the most valuable inputs, because they teach the system where its assumptions break down. Close the loop visibly: show the reviewer exactly what their feedback changed.

The best sign of a healthy feedback loop is that it bothers the expert with fewer obvious cases each cycle and surfaces sharper, more genuinely ambiguous questions instead. If reviewers keep catching the same easy errors, the loop isn’t actually learning.

A recurring lesson: the real context often lives in the underlying record, not the short summary the model was handed. If your agent only sees the summary, it will miss what a careful human catches by reading the whole file. Design retrieval so the system can reach the fuller context before it makes a judgment.

Keep a running, shared log of what changed, the review cadence, and the open questions — one place where the human and the system stay aligned.