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Monitoring the right metrics is key to evaluating the success of ChatGPT plugin development. First, track usage metrics: number of daily active users (DAU) who engage your plugin, number of plugin calls per session, and retention (how often users return). Next, measure performance metrics: average response time (latency) of your API endpoints, rate of successful vs failed calls (error rate), and server resource usage (CPU, memory). High latency or frequent errors will degrade user experience within ChatGPT. Also monitor conversion metrics if your plugin has goals (like sign-ups, purchases, task completions). For quality, track user satisfaction proxies: e.g., number of times users explicitly thank the plugin, negative feedback or aborts, and follow-on queries indicating the plugin didn’t fully satisfy. For ChatGPT plugin development, also monitor security and compliance: number of unauthorized access attempts, authentication failures, and data access logs. Use logging and alerting to detect anomalies (spikes of errors, unusual patterns). Use A/B testing if you release new features to compare engagement and outcomes. Periodically review your metrics and refine your plugin—reduce latency, widen user coverage, simplify flows, improve UX. Over time, these metrics will show whether your investment in ChatGPT plugin development is paying off and where you should focus next.