Let me preface this for you: using LLMs to write your code changes everything, and one huge factor that everyone needs to get over is the obsession with making the first iteration work. It's easy to watch things go sideways and panic. It needs to become just as easy to say "throw it out and start over."
That said, there are real ways to get a better result the first time. Follow some or all of these, and you'll feel a lot more prepared when it's time to put your app in front of actual paying users.
1. Start with a PRD, Not a Prompt
Make the AI plan before it builds.
Claude Code is currently the king of this, but I'm sure that'll be outdated 11 minutes after this post goes live. The point is: if you start with a plan, you'll know immediately whether you're heading in the right direction. Let your human brain read in human words what the non-human plans to do, and tweak it early before you get too deep in token town.
This doesn't mean you have to plan alone, either. Be the driver of the idea and see what your model comes up with. Just actually read the plan. You're already saving weeks of work here. No need to also save a 3-minute read.
2. Never Let It Add a Package Without Explaining Why
Mark my words: two years from now, the number of security incidents from compromised packages will skyrocket. If you let your AI add any package on a whim without any research, you're begging to be lumped in with the thousands of apps that'll get burned by poor security practices.
As an example, in Claude, work this into your base CLAUDE.md file: the model must get your approval before adding any package. As you get more advanced, you can even maintain an approved package list. Don't get taken down with the masses.
3. Ask for Error Handling Explicitly
It won't do it on its own.
Error handling, testing, scalability. Three things that are constantly overlooked when you're moving at AI speed. Work it into your flow. Create a slash command for it. Whatever it takes to make sure you handle it now instead of forgetting about it later.
Here's the thing: the further you go without adding these small things, the less useful they become when you finally get around to them. The context behind the feature you were building will be long gone. A little planning goes a long way when you work this fast.
4. One Feature at a Time
The "build me a full app" prompt is how you get spaghetti.
AI models understand their context window, and they'll try to be as efficient as possible. It's like being in the elementary school science fair. You can start your project three weeks early, spending a couple hours at a time on the presentation, the experiment, the documentation, mapping results, drawing conclusions. Or you can be the kid who pulled a moldy banana out of their locker and threw some words on paper overnight.
Given proper time and context, you'll always get better results working in chunks. Don't let your LLM get lazy. Force concentration with well-defined tasks.
5. Componentize Whenever Possible
Taking the above a step further: if you build three different ways to get data into your application without prior planning, you now have three different patterns you have to track for mapping that data, saving that data, and alerting users of issues. This is just good software development regardless of who's writing the code.
When planning, keep track of components that could be reused, and save that context wherever your AI model stores relevant info (like CLAUDE.md).
Instead of three unique data pipelines, build one data mapper and validator that all your entry points flow through. Planning mode can absolutely help with this. You just have to ask.
6. Set Up Your Environment Before Writing a Single Feature
Git, env variables, folder structure. The boring stuff matters most.
Be consistent and intentional about your setup. If you don't put your secret keys in an environment variable file, I promise you they will end up in git at some point, and that's basically an invitation to have your keys stolen. Always create a git repo, and always have the model commit as it goes. If you're not using git currently, think of it as creating save points you can easily roll back to when things go sideways.
On top of that: dockerize, dockerize, dockerize. A workflow built around Docker containers is way more likely to port cleanly to a live system and have predictable behaviors. Not to mention the ease of deployment, advantages in release tooling, and about a hundred other things that'll make your life easier when you actually ship to production.
7. Spend Five Minutes Now or Five Days Later
The whole point of vibe coding is speed. You're supposed to move fast. But moving fast in the wrong direction just means you get to the rewrite faster.
None of this is about slowing down. It's about spending five minutes upfront so you don't spend five days untangling something later. The AI is incredibly powerful. It just needs a better copilot.
Build the habit now. Your future self, the one staring at a codebase that actually makes sense, will thank you.
Already past the point of prevention? We run production readiness audits on AI-built codebases. We'll tell you exactly where the gaps are and how to fix them.