A blog post offers a five-minute triage method to distinguish between rate limits and truncation errors when interacting with free-tier Large Language Models. The author provides a Python script that sends three probes to an LLM endpoint: one to check basic reachability, a second to test parameter validation (like max_tokens), and a third to assess generation speed and finish reason. By analyzing the HTTP status codes, response times, and finish reasons from these probes, users can accurately diagnose whether their LLM calls are failing due to server-side throttling, invalid parameters, or output truncation. AI
IMPACT Provides a diagnostic tool to help developers efficiently troubleshoot LLM API interactions, saving time and resources.
RANK_REASON The item describes a practical script for diagnosing LLM API issues.
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