When facing unexpected errors in a live sorting algorithm, follow a strategic approach in debugging and resolving the issue. Key points to remember: - Identify the point of failure: Observing the logs might pin point the source of the errors. - Isolate the faulty point: This allows you to monitor the failure point, identify the root cause of the errors and create fallbacks. - Rollback to a stable version: This allows you to mitigate further errors and observe the system behaviour. Here you can have a better understanding of the conditions that lead to the unexpected errors. - Test and Optimise: Write test cases using valid and invalid data. This tests the algorithm's resilience and helps improve performance, monitoring.

Based on what I've read and heard, when a live sorting algorithm throws an unexpected error, the best approach is to stay calm and tackle it step by step. The first thing to do is pinpoint the issue—checking logs and error messages usually provides a good lead. If the error is critical, rolling back to a stable version can help keep the system running. Also, having real-time monitoring and logging in place makes it easier to catch issues early and build a more resilient system for the future.

Stay calm, analyze logs, and isolate the issue. Use debugging tools, test edge cases, and apply quick fixes if needed. Prioritize stability, then refine for efficiency.

When unexpected errors arise in a live sorting algorithm, quick thinking is crucial. Instead of wasting time on manual debugging, simply copy-paste the code into AI tools like ChatGPT, Copilot, or Gemini and ask, “What are the problems in this code? How can I fix them?” Review the AI’s suggestions, apply the fixes, and then develop unit tests to verify the solution. Finally, test and monitor for performance issues before deploying.

In my opinion we rollback to a stable version, and then try to apply a path by debugging and understanding where the problems arise from.If a stable version is not find just try to understand the incident by looking at the logs, and apply the patch.Usually should be some tests and the tests could have missed some edge cases which might be spoted in the logs, checkout a branch, pulling the latest version of the master, run and reproduce the errors locally, then add the tests, rerun and make sure the patch works, then ask for emergency ticket release and release your code, not before the builds and one of more member of the team aprove the changes, including someone from the business side, if necessary.