Debugging in the Age of AI

In the past, junior developers often began their debugging journey by Googling error messages. This approach, while frustrating due to the sheer volume of irrelevant results, forced developers to dive deep into their own code. To find the right answer, they had to iterate, experiment, and ultimately develop a profound understanding of the problem itself. This iterative process was a powerful learning tool, leading to both working code and a solid grasp of underlying concepts.

This rigorous approach was also reflected in communities like StackOverflow. You couldn't just post "It's broken" or "Help me fix it." The community actively encouraged users to perform investigative work, gather context, and articulate the problem clearly to receive meaningful help. This taught developers to break down complex issues, identify symptoms, and present all necessary information. These are invaluable skills for effective debugging.

Today, many junior developers turn directly to LLMs, feeding them error messages and code snippets. While this can quickly lead to working code, it often bypasses the crucial step of understanding the problem. The LLM provides a solution, but the developer may not understand why that solution works or what the root cause of the issue was. This can hinder a junior developer's ability to level up. They miss out on the investigative skills honed by traditional debugging methods and community interaction.

The time you invest in describing the problem is often the time you spend resolving it.

There's a lot of talk about LLMs making developers 20%, 30%, or even 50% more productive. However, my observations suggest a more nuanced reality. While LLMs can generate large chunks of code, the time spent reviewing, understanding, and often correcting that generated code can significantly offset any perceived productivity gains. It's like being handed a complex puzzle already assembled. You have the solution, but you don't understand the pieces or how they fit together.

The core issue lies in how we interact with LLMs for problem-solving. We often jump to asking for a solution without first fully clarifying the problem for ourselves. The very act of describing a problem in detail, articulating its symptoms and constraints, is often the key to unlocking the solution. When you force yourself to articulate the issue clearly, you gain a deeper understanding, and the solution often becomes apparent even before the LLM has a chance to respond.

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I recently helped a developer with a UI issue where a user could drag and drop items to reorder a product list, but the order would be garbled upon page refresh. An LLM, presented with "Why are items not keeping the provided order?", suggested various sorting libraries or tools. While these might seem relevant, they miss the fundamental question: "What is the actual problem in the first place?"

A peek at the code revealed the true culprit: an incorrect algorithm that updated each entry after a reorder. The LLM couldn't "see" this underlying database query, leading it to recommend external solutions for a problem that was internal to the application's logic. The developer, in turn, spent an inordinate amount of time researching sorting libraries instead of acting like an investigator.

My approach was different. Instead of looking for a quick fix, I opened the code then saw the call to the database. I opened the database, changed the update query to a select query, and ran it. This allowed me to see the data being returned with the current parameters. Immediately, the flaw in the algorithm became apparent. A quick adjustment, and the problem was resolved in a matter of minutes.

The Power of Clarification: Your Best Debugging Tool

This may be an anecdote but it highlights a critical point: working with an LLM or an agent does not substitute for thorough debugging and investigative work. The time you invest in describing the problem is often the time you spend resolving it. In the process of clarifying the issue for yourself (or for an LLM), you inherently deepen your understanding and often stumble upon the solution.

For junior developers especially, cultivating this skill of problem articulation and systematic investigation is paramount for long-term growth. LLMs are powerful aids, but they're most effective when used as collaborators in a well-defined debugging process, rather than as a substitute for critical thinking and deep code analysis.

So, the next time you're faced with a tricky bug, try this: before you even think about prompting an LLM, take the time to clearly and precisely describe the problem out loud or in writing. You might be surprised at how often the solution emerges from the clarity of your own understanding.

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