How a 90-Line Python Script Can Catch Silent Behavior Shifts in Free LLM APIs

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Free large language model endpoints can silently change behavior through model swaps, temperature adjustments, or added safety filters, often without any changelog or notification to developers. These invisible shifts typically cause gradual quality degradation that users notice before developers do, yet they rarely trigger error logs or test failures. A proposed solution is a nightly drift check: a lightweight script that runs a fixed set of representative prompts against the endpoint and compares outputs to a previously recorded baseline. The harness, written in around 90 lines of Python, is designed to run as a cron job or GitHub Action in two modes — one to record baseline behavior and another to flag deviations. The article, disclosed as part of MonkeyCode's product outreach, uses MonkeyCode's free LLM server as the reference endpoint to demonstrate the approach.

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