This article details a developer's experience using MonkeyCode's free LLM tier for code review, highlighting the importance of testing the AI reviewer itself. Through a pairing session, five critical questions were developed to empirically verify the LLM's performance, focusing on its ability to catch bugs, produce stable JSON output, and avoid false positives. The process involved creating a Python script to automate these tests, sending code samples to an OpenAI-compatible endpoint and analyzing the responses. AI
IMPACT Provides a framework for developers to rigorously evaluate LLM code review tools before relying on them.
RANK_REASON Article describes a method for testing an LLM tool, not a new release or significant industry event.
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