You Never Stop Debugging
I don’t think debugging will ever stop because things never stop changing.
We often think of debugging as something reactive, a step we take after something breaks. It's treated like a chore. A necessary evil to "get back on track." But the longer I have spent building products, especially in AI and fast-moving environments, the more I have realized something different: I have been looking at debugging only from one angle. What I thought was just fixing things that broke has turned into something I find myself doing constantly: a way of thinking that shows up everywhere in my work.
It Starts With Code, But It Doesn’t Stay There
Most of us learn debugging in code. You hit a 500, a failing test, and log everything, throw in some print statements, and stare at a stack trace until something clicks. Over time, you get better. You learn to isolate, to reproduce, and to reduce.
But then, as you work on more complex systems, debugging shifts. It shows up not just when you hit a bug in your codebase, but when a user drops off unexpectedly, when a system slows down without explanation, when a product decision doesn't work out the way you had hoped, and sometimes even when our assumptions are wrong. The skill evolves from just fixing bugs to a way of thinking, one that stays relevant no matter how much the tech changes.
What I have found is that I am not always trying to fix bugs anymore. I am trying to become someone who can debug anything that comes up.
Dealing with Gray Areas
Most real-world systems don't break with clear error messages. They degrade subtly. A feature feels sluggish. A model's responses seem "off." An integration half-fails without throwing.
In AI applications especially, you won't always get deterministic failures; you get symptoms: unexpected behavior, weird edge cases, and users reporting "it feels broken" without being able to say how.
When you are building AI systems, debugging takes on a whole new dimension. There is no single root cause, just a tangled mix of model behavior, data assumptions, retrieval failures, evaluation noise, and blurry feedback loops. The bugs aren't always obvious, and sometimes, they don't even look like bugs.
I have spent hours staring at outputs, asking myself: Is this bad behavior, or just expected ambiguity? Did the model hallucinate, or did we prompt it wrong? Debugging AI isn't about fixing lines of code. It's about tracing the problem across layers: product, infra, model, expectations, and even user perception. It's one of the clearest reminders I have gotten that debugging is not just technical; it's about the entire system.
I have found that it is not always about having the answers but being relentless about finding the questions. It has given me a way to chip away at ambiguity, one hypothesis at a time.
Navigating the Unknown
Systems are always going to break, and when something breaks, it's not about panicking but staying curious. We can't predict everything. But we can get better at navigating the unknown. That's what's made debugging feel like such an enduring skill.
Every system failure becomes an opportunity to understand something better. That broken feature reveals an edge case that was not considered. That misbehaving model shows where the data assumptions were wrong. That confusing UX flow highlights a gap between what users want and what we built.
You can read best practices all day. But real insight often comes from debugging the mess. That's where systems reveal their true shape.
The Muscle That Never Stops Growing
I have come to see that I never stop debugging, not because systems are bad, but because systems are alive. As engineers, we are constantly navigating change: shifting requirements, new constraints, evolving tools, and unexpected outcomes.
It's taught me to zoom in and zoom out, to trace problems across logs, layers, teams, and sometimes across months of decisions. It is how I have developed some sense: not just in code, but in architecture, product, and trade-offs.
The deeper I go, the more I realize: I never stop debugging because building is just debugging in motion. Building and debugging aren't separate phases. They blur. Every feature is full of tiny, invisible bugs in understanding and in decisions, and we only find them by being willing to look closely.
Some days, debugging is about fixing a crash. Other days, it's sitting with a vague discomfort that something's off and trying to trace where it began. Either way, it's the same muscle.
New frameworks will come. Languages will change. But I have found the ability to debug, to calmly untangle the mess, and to move toward clarity never stops being useful. It's the most transferable skill I have encountered. It works whether you are knee-deep in logs or navigating an issue faced by the user. It has become how one can make sense of chaos and still ship things that work.
I don’t think debugging will ever stop because things never stop changing. And the more you practice, the better you get at spotting what's going on underneath the surface.