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--- ai_error_tracker [2025/05/26 23:31] – [Usage Examples] eagleeyenebula
+++ ai_error_tracker [2025/05/26 23:33] (current) – [Best Practices] eagleeyenebula
@@ Line 256: / Line 256: @@
 . **Persist Logs Securely**:
-   - Regularly back up the SQLite database or use a centralized logging system in production.
+   * Regularly back up the SQLite database or use a centralized logging system in production.
 . **Use Severity Levels Appropriately**:
-   - Assign severity levels (`LOW`, `MEDIUM`, `HIGH`, `CRITICAL`) to prioritize debugging.
+   * Assign severity levels (**LOW**, **MEDIUM**, **HIGH**, **CRITICAL**) to prioritize debugging.
 . **Extend for Specialized Use Cases**:
-   - Include additional metadata fields, such as stack traces, user IDs, or application states.
+   * Include additional metadata fields, such as stack traces, user IDs, or application states.
 . **Monitor and Analyze Trends**:
-   - Periodically analyze logged errors to uncover common problem areas or recurring bugs.
+   * Periodically analyze logged errors to uncover common problem areas or recurring bugs.
 . **Integrate with Monitoring Tools**:
-   - Pair with external alerting tools, such as email notifications for critical errors.
+   * Pair with external alerting tools, such as email notifications for critical errors.
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 ===== Conclusion =====
 The **AI Error Tracker System** provides a structured, scalable, and easy-to-use framework for logging and analyzing application errors. Its extensible design and rich feature set make it a valuable tool for modern software workflows, supporting everything from debugging to long-term trend analysis. With features like severity-based filtering and dynamic querying, the ErrorTracker simplifies error management while offering flexibility for complex use cases.