You Ask, I Answer: Managing Code Quality In Vibe Coding

In today's episode, C D asks, how do you manage code quality, prevent duplicated code, and handle bad directory structures in vibe coding? How uh do you write it all down in advance? You microspec it, and and how are you dealing with code quality? Okay, so uh code quality, a couple things. Number one, you have to have a spec.

You have to have a design spec. There is a great open source utility that is free uh called open spec that I strongly recommend. If you are if you are new to vibe coding, I would I would almost make open spec mandatory because it will teach you how to write specs and it will or will at least teach you how to prompt an AI to write good specs, which you need. You cannot you cannot create great code without a good spec. Second thing, you have to have a solid, really good uh product requirements document or PRD.

Uh a PRD is the difference between good and bad because if you are if you uh have a PRD that clearly articulates this is exactly what my code is supposed to do, then AI has the ability to review the PRD and essentially fact check its output against it. This is something I do a lot. So I will say to an AI tool, at the end of every sprint, review your code against the PRD and make sure that you have created code that is aligned to it and that is not um a hot mess or that does not implement you know you you one of the things these tools will do if you just let them go is they will add new stuff to your code and to your code base that you didn't ask for. You'll add features because remember we trained these tools on human on humans output on human code, and as a result, you get we were uh a mathematical average of human code. That's not necessarily a good thing.

There's an awful lot of code online in places like Stack Overflow and stuff that is not high quality. So you have to bring the quality with you. You have to you have to create the quality. Um PRD, really important to have that. Other things to have is really good QA cycles.

So you if you're using a tool like Claude Code or Open Code or Quen code, they have things called hooks. Hooks are basically automated triggers. You can create a hook that says at the end of this process, do you know, fire off your linting tools to clean up your code, fire off your uh testing tools. The magic sentence that I put in every PRD and every coding hook is goes like this. You must have 100% test coverage, and your unit tests and your end-to-end integration tests must all pass at 100%.

Less than 100% is failure. 100% coverage and tests passing is success. And you say this is an immutable rule because if you go online and you read and you just Google in old-fashioned Google what is an acceptable amount of test coverage in your code base, you will get an answer that says about 80%. I'm sorry, these are AI tools, these are not humans that we're working with. AI tool, I can understand human being like, do we really need an edge case for this or that or the other thing?

With an AI tool, you're like, no, 100%, nothing less than 100% is is acceptable. And if you give it that directive, it will generate much higher quality code because it says, I understand that 100% test coverage and 100% passing unit tests and end-to-end integration tests is mandatory. And it will it will do as you tell it to do. That is how you generate really high quality code. The other thing that you should do, you should be doing is you should be having the tools be equipped with good deterministic quality tools.

So for example, in Python, there's obviously PyTest. There is Rough, which is the uh R UFF. Ruff is the coding tool that uh checks Python syntax, does linting and cleanup and stuff like that. There is Bandit. Bandit is a deterministic security tool that will check your code for obvious flaws.

There is Vulture. Vulture looks for dead code, looks for stuff that is in your code base that is just totally unused that you're that the AI wrote in there. Um there is MyPy, there is interrogate, there is Hypothesis, there is SEMgrip. Um every programming language that I know of has these quality tools, these testing suite tools. And the thing is this, you don't have to do them.

You don't have to sit there and run them manually. You in your claw.md or your agents.md say these are the tools that are available to you that are installed on this system, and you should install them on your system. These are the tools that are available to you, and this is what your quality checking algorithm should look like. Um, one of the things that I do in my vibe coding environment, I have three tiers of QA checks. Uh I have like on tier one, which runs every sick, which fires off after every single completed task, is stuff like Rough.

Uh Ruff, Bandit, MyPy, and PyTest are the four that fire off after every single task is completed because I want AI checking its code the moment it says it's done. When it says I did the thing, I like to say, no, you didn't, you didn't do the thing until you have checked your code with these deterministic tools, and they return zero errors. Uh then you have more advanced tools like sem grep and stuff like that, uh radon, xenon, etc., that are like tier two and tier three because they take more time and they create, you know, they highlight stuff and they can slow down your development significantly. Those are things you use after the end at the end of a milestone, right? So you have a task, like write this thing.

You have a milestone, like here's a bunch of things that I wrote, and then you have obviously uh user acceptance and and you know and and heavy deployments. At milestones, you run tier two and tier three. You run those different quality checks that are more intensive, and then you say, as always, right? Um the thing is I always say is no level of error is acceptable, right? So if if xenon says uh, or in if radon and xenon in Python say, you know, this this file is a is scored a C, and my rules say A and B only, uh Claude in particular tries to be lazy.

Claude will say the average score of your files is a B. So the job is done. And I always well, I have it as a hook that fires. It says, if any file is below a B, you have to fix it. And so it will be grudgingly do that.

So these are just some of the things that I've discovered as I'm building stuff. Um do as much as you can deterministically. If I had to sum it all up, it would be do as much as you can deterministically and feed the output of deterministic tools into the into your coding tool so that it knows what to do, so that it knows what is and is not acceptable. If you leave it just to the coding tools, you will get a hot mess. You will get massive amounts of accumulated technical debt, you will get garbage everywhere.

And here's the thing good quality code in general has been a solved problem for a very long time. And we have these great tools that I've mentioned in this episode that will help you clean up that your your AI's sloppy coding that it learned from the internet and turn it into high quality code. So give that a shot. Thanks for tuning in. Talk to you on the next one.

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Christopher Penn has written artificial intelligence books such as The Intelligence Revolution and AI from Marketers, An Introduction in Primer. Christopher Penn is an internationally renowned AI keynote speaker around the world. Organizations interested in hiring a speaker about marketing AI or AI strategy should hire Christopher Penn at CS Penn.