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Navigating the Traps: Common Pitfalls in Troubleshooting

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Picture this! You’re debugging a critical issue under tight deadlines. Your instinct? Copy the error message into a search engine. The results pour in—Stack Overflow threads, GitHub issues, blog posts. You try the top solution, but it doesn’t work. Hours later, you’re lost in dozens of tabs with no clear solution. Sound familiar?

Fast Paced development -Need skill to take a Step back

In today’s fast-paced development world, this scenario is all too common. Hours can be wasted in this loop without clarity on whether they’re getting closer to solving the problem or drifting further away. While internet resources and AI tools have transformed troubleshooting, they’ve also encouraged blind reliance on external solutions without understanding the problem. On top of it, need for an urgent fix from the business, the urge would be finding a quick band aid instead of a right, long term solution. In such high visibility and stressful situation, one needs a skill to take a step back, a minor one at least to see the problem in detail and then find a solution

A common pitfall in troubleshooting is over-reliance on internet resources or AI tools. While helpful, misuse often results in wasted time, code bloat, and missed learning opportunities. Let’s explore why this happens and how to avoid it. Whether you are troubleshooting Single Sign-On (SSO) in Azure, or an unexpected application error or fixing a issue in an application which you have minimal idea about. Instead of focusing only on the error message, start by understanding the programming language — Python, Java, or.NET, the library from a reliable source —since each has nuances critical to solving the issue.

When you step back and learn from previous experience, with most of the technologies and platform having a comprehensive documentation, FAQs, begin with referring to official documentation for your implementation and evaluate whether the provided samples align with your environment, and ask below basic questions:

Does the sample assume a setup different from yours?
Does it require specific configurations?
Understanding and adapting the code to fit your environment is crucial.

The Risks of Blind Copy-Pasting

Blindly copying entire codebases from online resources might seem to be a easy and quick option, but when you do it without comprehending what you are copying, is a recipe for long-term problems, such as:

Incompatibility with Your Environment

Most code snippets online address specific problems in specific setups. For example, if the code isn’t tested behind a proxy, you might need to add a proxy agent to make it work.

Code Bloat

Adding unverified or unnecessary code from multiple sources creates a messy, unmanageable codebase over time.

Missed Learning Opportunities

When you don’t understand what the code does, troubleshooting the next issue becomes even harder.

Best Practices to Avoid Over-Reliance on internet resources & AI

Understand Before Applying

Break down the external code to smaller chunks, test it separately, and ensure you fully understand its functionality before integrating it into your project.

Refer to Reliable Sources

Prioritize official documentation or trusted resources over random internet forum

Incremental Implementation

Apply only the parts of the solution you need instead of copying an entire block of code. This reduces the risk of introducing unnecessary dependencies.

Leverage AI Tools as Assistants, Not Decision-Makers

AI tools can provide suggestions or guidance but shouldn’t be blindly trusted. Always verify their outputs against your context.

Conclusion

While internet resources and AI tools are valuable, their effectiveness depends on how you use them. Treat them as assistants, not solutions. Always cross-check online solutions with official documentation and adapt them to your specific context. In doing so, you’ll avoid code bloat, understand your systems better, and become a more effective troubleshooter.