Tutorial Shows How to Display the Serving Tier Beside AI Drafts for Reviewers

A tutorial argues that AI review tools should display which model tier served a draft, since reviewers cannot judge output they have no evidence about. It uses an example where a reviewer approved a draft that had silently dropped a refund clause without knowing the model or server involved. The guide walks through running a probe to capture serving details, rendering a tier strip with fields such as model, finish reason, latency, and context status, and treating unknown values as valid states. It also describes writing stop conditions and testing reviewer behavior across scenarios like context overflow, rate limits, and mid-task failures. The piece is disclosed as part of an outreach effort for MonkeyCode, an open-source AI coding project, though its claims are presented as items to verify.