Disclaimer: This post is mainly aimed at people who are already experimenting with “vibe coding” or AI-assisted development. I’m not saying it’s the right way for everyone, just sharing what has worked for me and what I’ve learned from using AI alongside a traditional development background. I’ve seen countless posts over the years since ai, saying that “vibe coding” is much harder than it looks, or that AI is actually pretty dumb once you try to build something serious. And to an extent, I understand why. AI can easily misunderstand what you mean, take the wrong approach, or produce something that looks fine in isolation but doesn’t fit the wider system you’re working on. It can be incredibly useful, but it still doesn’t replace human judgement, technical understanding, experience, and the ability to see the full picture. I was developing long before AI became part of the workflow, so for me, AI doesn’t replace the thinking. I still need to understand the full blueprint of what I’m building, the architecture, database schema, authentication flow, frontend state, security, how features connect together, and what a change in one place might affect elsewhere. There have been plenty of times where AI could write the code, but I still had to define how the system should actually work. Knowing the project in detail is crucial, otherwise it’s very easy to accept something that technically works but creates problems later. I think this is where a lot of people go wrong. They start with a vague idea, let AI make too many architectural decisions for them, then keep stacking fixes on top of fixes without fully understanding what changed. Eventually the codebase becomes inconsistent, AI loses context, and every new feature starts breaking something else. The bigger the project gets, the worse that problem becomes. If you know what you’re building and, more importantly, how it should be built, AI can be incredibly effective. The moment you get side tracked or stop understanding the system yourself, AI starts filling in the gaps by guessing. I personally know people who try this, want to get into creating the apps but end up spending so much time, weeks, or even months and end up quitting. Where AI really shines for me is execution. It speeds up repetitive work, helps explore different approaches, and sometimes introduces ideas or solutions I might not have considered immediately. But I’m still the one steering it, reviewing what it produces, and deciding whether it actually makes sense for the application. That’s why I think AI becomes far more powerful when you already have the technical knowledge behind it. Remember, if you get lost yourself, so will AI eventually. What seems to work best for me is keeping AI focused and giving it context in smaller, controlled steps. I don’t ask it to “build the whole thing.” I define the feature, explain how it fits into the existing system, tell it what should and shouldn’t change, and then review the result before moving on. I also try to keep a clear architecture in my own head instead of relying on AI to remember the entire project for me. Another big one is not blindly accepting fixes. If something breaks, I want to understand why it broke before asking AI to patch it. Otherwise you can end up with five fixes covering up one bad decision. AI is much better when you use it to help solve a problem you understand, rather than asking it to figure out the whole problem for you. So my basic rule is: plan first, give clear context, make small changes, review everything, and stay in control of the architecture. That’s where AI stops feeling like “random vibe coding” and starts feeling like a serious development tool. Heres a few habits that i tell to anyone who asks me how I develop with AI: Plan before you prompt. Know what the feature is supposed to do, what data it needs, and how it should fit into the rest of the project. Break work into small pieces. Don’t ask AI to build an entire application in one go. Build one feature, test it, understand it, then move on. Read the code it gives you. Even if it works, make sure you understand why it works and what else it might affect. Ask “why?” instead of only “fix it.” If something breaks, get AI to explain the cause before accepting a patch. Keep track of your architecture. Know where your database logic, authentication, frontend state, APIs, permissions, etc. actually live. Challenge the AI. Ask whether there are downsides, edge cases, security issues, or a simpler approach before committing to a solution. Learn the fundamentals alongside it. The more you understand the programming yourself, the easier it becomes to spot when AI is guessing. This step gets easier even with vide coding. The biggest thing I’d recommend practising is being able to explain your own application without AI. If you can clearly describe how the system works and why it was built that way, AI becomes a massive accelerator. If you can’t, it can very quickly start leading you instead. In one of my most recent projects, I completed filetro.com editor which is a fully browser-based image editor that runs locally on the user’s device, with no software download required. Before I started building it, I had already planned out how it should work, how the different parts would connect, what needed to happen locally, what needed to happen on the backend, and how I wanted the overall experience to feel. I then used AI alongside that plan to help with the implementation, testing, problem solving and repetitive coding work. There were plenty of hurdles along the way, and I’m sure there always will be, but working through those problems is part of the development process. I think there’s always more to learn as you build more, gain experience, and keep experimenting. AI can even introduce you to approaches, tools, or ideas you might not have come across before, so the learning can go both ways. The important part is still understanding what you’re doing and being able to judge whether those ideas actually make sense. When you get that balance right, AI can eventually become much more than a shortcut for writing code. It can help you work faster, explore ideas more freely, improve your creativity, and turn things that might once have felt unrealistic or too time-consuming into something you can actually build. I’m curious how others see it. Are you happy using AI as part of your development workflow, or do you still prefer working without it? Has it genuinely made you better at building things, or just faster? And if AI tools disappeared tomorrow, would you happily go back to the old way of working, or would it now feel like losing a major part of your toolkit? Me personally, i’m not necessarily a big fan of AI when it comes to things other than helping me to code. I think AI has definitely helped me now go from slowing down or turning projects down to actually doing them, since it’s almost like having 10 workers by my side. I went from doubting to let’s do it and having fun actually. submitted by /u/Filerax_com
Originally posted by u/Filerax_com on r/ClaudeCode