Mind Readings: How to Vibe Code Well, Part 5

Welcome back. This is the fifth and final part in our series on how I vibe code. This is debugging in QA. In part one, we talked about asking good questions. In part two, we walked through requirements documents.

In part three, we built the work plan. Part four, AI did the typing, right? Which is essentially what it was doing. And then in this part, we're gonna do the debugging because there is absolutely positively no way whatsoever that the first draft of what it created was at all usable. I mean, it's there on disk.

We can see it, we can see all the files that are there. There, and they're they're pretty decently sized. But I can just tell just in the file sizes alone, there is no way this thing is functional. So let's talk about the process for doing this kind of debugging. We want to um we want to feed this project back into generative AI to have generative AI run its QA process.

And so I have a uh custom piece of code in Python, which is is not super complicated. And what it does is type Python data diarist. All it does is sew together the documents in a folder and says, hey, here's the documents that are in that folder. Now uh it's bundled together into one file. And it's called data diarist.

There it is. There's there's the file. That's that's all it does. Super simple. But what we want to do is we want to use that with our generative AI tools to evaluate our code to make sure to see what it did and what it didn't do.

So we're gonna say uh we're gonna start the QA process. Uh let me see if I have a pre-star. I can't remember if I have a pre-stored prompt for this or not. I think I do. Yes, I do.

It says we're gonna put in our our Chrome best practices. Remember, we always keep our best practices and system instructions, and we're gonna say, whoop, not that. Uh evaluate my code versus the PRD and the work plan. What's done, if anything, what's not done, if anything, thinks in terms of QA. What's good, if anything, what's bad, if anything, what's missing, if anything, and what's unnecessary, if anything.

And we're gonna give our code base. I'm gonna turn on Google search here. 38,000 tokens, about 30,000 words. So this is a decently sized uh piece of code, and let's see what it comes up with. All right, here comes the output.

It says the project has a foundational back end that aligns reasonably well with the work plants phase two. However, critical stuff is missing. Here's what's done. And it's making a nice long list. And here's what's not done.

Uh-oh. Holy smokes. Look at all the stuff in here that is just not done right or is or is not proper. What's good, what's bad. You've got a bunch of uh things that are really screwed up.

What's missing, what's unnecessary? There's there's nothing unnecessary. This is not ready for testing. So now we've got our our layout. Remember what I said at the beginning of part two, or maybe it was part three.

Plan big, act small. So our small model, we're using um Quen3. Our small model can't really do the big picture stuff. It can type, right? That's that's really what it is.

It's a typist. And so we're gonna say from this uh set of feedback, build a work plan to fix all the bugs, right? Focus on root causes, don't patch over systems. Highly detailed set of instructions, file by file for the developer to use to update the code base. Files should be ordered from viewest to most dependencies, must contain the file path.

The changes needed, the upstream and downstream dependencies to keep in mind the overall intent of the file. The work plan must be granular enough that the developer needs no other information to make the necessary code. Feel free to include diff samples appropriate for use of systems like Git. Build the detailed work plan and markdown. So this is going to be our next phase where we say, hey, you screwed up.

Here's all the stuff that you forgot to do. And you can see in this cycle, how much more detailed the feedback is. It's saying, Here's the different pieces that you just didn't have. The QA cycle, this cycle is going to go on. Probably three, four, five, six iterations where you're going to go back and forth, and every time, more stuff is going to get fixed.

It will never be in one shot. But the advantage of doing it this way is that you're letting the typist model, the act model, get clear instructions and it just implements them. And you're reserving your thinking all in one shot in the big planning model, the state of the art planning model, that if you were to use it just in the interface itself, two things happen frequently. One, it will send you a bill, all the size of which you would not believe. I saw a post the other day saying Claude 4 did one task, one prompt for seven dollars and sixty cents, which you know, okay, that's the price of like a burger, but we've clearly done more than one task.

And two, very often, these tools can get caught in loops. They get they they start chasing their own tail. If you go back and forth and say, okay, here's my code base, here's what I've changed, check it out, and you do this QA process repeatedly, you're less likely to run into those kinds of loops that cause the coding tool itself to just get caught up, just get lost. So we're gonna let this run. Um, I can guarantee by the end of this uh broadcast, we will still not necessarily have a functional piece of software.

I'm in the midst, like I said, of building an uh another piece of software that is, you know, it's in I think version four of its of its QA loops. But this is how you get to working software. This is the process for doing and doing it as inexpensively as possible. If you're already paying for Chat GPT or Claude or whatever, and you're paying the 20 bucks a month, you don't want to add a big API bill on top of that. If you want a fast lightweight model, you don't have to host on your own.

Deep seek is good if you are doing stuff that's not confidential, that's gonna be in the public. Gemini Flash is really good at being a typist, and it's very, very cheap. Um, your average uh session for like an hour or two of coding with Gemini Flash is gonna be four or five dollars. Um, if you use Gemini Pro, it's gonna be four or five hundred dollars. Um, you can you can rack up some really big bills with that.

So to wrap up, the vibe coding process is software development. It is classical software development. Nothing has changed except the amount of typing that you do. You still have to have the ideas, you still have to flesh out the requirements, you still have to help build a work plan, you still have to guide the debugging process, the QA process and things where the machine can, yes, it can QA its own code, but with your help, with your prompting, with your management of the process, and ultimately, what you do end up with are great functional pieces of software that have fewer bugs than human-led in many cases, because you're having the machines check their own work and alternating between that plan big act small framework. I would encourage you to think about the kind of software that you would want to have exist in the world that doesn't exist right now.

And I would encourage you to start small. Start with a Chrome extension or a calculator or something on your website so that you get some practice, put some reps in to doing the vibe coding process as I've outlined it, and then once you've you're comfortable with that, then start moving on to bigger and bigger and bigger things until you get to the point where yeah, you're building out large applications, production ready applications that have been debugged and checked for security and all these things to make really great code. But the core message is this you can be a developer, you can bring your ideas to life, bring them into the world using generative AI. It's doing all the typing, you're doing all the thinking. We're never just letting the machine go off and do its own thing, ever.

No, never do that. We are instead saying, let's collaborate together and build something great together. That's gonna do it for this series. Thanks for tuning in. I'll talk to you on the next one.

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