It has been about four months since ChatGPT launched, and I have been using it nearly every day for development work. Not as a gimmick or an experiment, but as a genuine part of how I build software. Here is what has changed in my workflow - and what has not.

Rubber Duck Debugging, But the Duck Talks Back

The most valuable use case has been debugging. When I hit a confusing error, my old workflow was: read the error message, search Stack Overflow, scan through answers, try solutions, repeat. With ChatGPT, I paste the error along with the relevant code and get a targeted explanation of what is likely going wrong.

The key insight is that ChatGPT is not just giving me the fix - it is explaining the underlying cause. Stack Overflow answers often tell you what to change without explaining why. ChatGPT walks through the reasoning, which means I understand the problem better and am less likely to hit the same issue again.

That said, ChatGPT is confidently wrong about 15-20% of the time. It will explain a plausible-sounding reason for a bug that turns out to be completely unrelated. You need to verify its suggestions, not trust them blindly.

Architecture Discussions

I work solo on several projects, which means I do not have a colleague to bounce architecture ideas off. ChatGPT has partially filled that gap. When I am designing a new system, I describe the requirements and constraints and ask it to evaluate different approaches.

It will not give you a novel architecture you have never considered. But it is good at listing trade-offs, pointing out failure modes you might overlook, and challenging your assumptions. I treat it like a knowledgeable colleague who has read a lot of engineering blogs but has never operated a production system.

Learning New Libraries

This is where ChatGPT has saved me the most time. Instead of reading through documentation pages to find the specific API call I need, I describe what I want to accomplish and get working code examples. Last week, I needed to set up server-sent events with Express.js. Instead of piecing together the approach from blog posts and docs, I asked ChatGPT for a working example and had a functional implementation in minutes.

The caveat: ChatGPT's training data has a cutoff, so it does not know about recent library versions. I have been bitten by this a few times when it generates code using deprecated APIs. Always check that the syntax matches your actual library version.

Writing Tests

I have always found writing tests tedious, not because I do not value them, but because the setup is often repetitive. ChatGPT is excellent at generating test scaffolding. I give it a function and ask for unit tests covering the main cases and edge cases. The generated tests usually need some adjustment, but they cover scenarios I might have skipped if I were writing them manually.

This is probably where ChatGPT has had the most measurable impact on my code quality. I now write more tests per feature because the friction of writing them has dropped significantly.

What I Do Not Use It For

I do not use ChatGPT for writing production business logic. The risk of subtle bugs is too high for code that handles money, user data, or security. I also do not use it as a replacement for understanding. If ChatGPT writes a solution I do not fully grasp, I either ask it to explain step by step or I go read the documentation myself. Using code you do not understand is technical debt you are taking on deliberately.

The Workflow

My daily development now looks something like this:

  1. Plan the feature or fix I am working on.
  2. Write the core logic myself - the parts that require domain knowledge and careful thought.
  3. Use ChatGPT for boilerplate, test generation, and unfamiliar library APIs.
  4. When stuck on a bug, describe the problem to ChatGPT before searching Stack Overflow.
  5. For architecture decisions, discuss trade-offs with ChatGPT, then validate with documentation and my own experience.

The fundamental skill shift is learning to communicate precisely with an AI. Vague questions get vague answers. Specific questions with context, constraints, and examples get useful code. This is true for searching Stack Overflow too, but the bar for specificity with ChatGPT is even higher.

Where This Is Going

We are clearly at the beginning of something significant. ChatGPT in its current form is useful but limited - it does not know your codebase, it cannot run your tests, and it hallucinates. But the trajectory is obvious. In a year or two, AI tools will have full codebase context, persistent memory across sessions, and the ability to execute and verify their own suggestions. That will be a much bigger shift than what we have today.

The developers who will benefit most are the ones building the habit now - learning how to collaborate effectively with AI tools while they are still relatively simple.