5 Essential Vibe Coding Tips For Newbies

The term "vibe coding" has been flung around a lot on social media these days; It's rapidly joining an army of slop terms that can throw the average pre-Gen Z internet user out of the loop. But what exactly is vibe coding? As the name implies, it's simply coding based on "vibes" — practically, with very little actual thought and effort. Now, if you know anything about programming and the field of software engineering, the concept of vibe coding is pretty much antithetical to the experience. By design, programming is a very involved field that brings abstract ideas to real life via a computer screen.

However, harnessing the power of large language models like OpenAI's ChatGPT and Anthropic's Claude is where much of the vibes reside. It's more of a laid-back approach to programming. Picture a futuristic electric car. You know where you're going, you know how to get there; You even know how to drive. However, you really don't feel like doing all that work. So, you "talk" to your car and give it instructions to get you to your destination. That's vibe coding in a nutshell.

While this sounds like sunshine and rainbows, there are a few things you'll need to remember. LLMs are not infallible, and you'll run into a host of problems if you just wing the vibe coding experience. Your products won't scale, and you'll burn through your prompt credits really quickly. Here are five tips to avoid running into issues like these.

One of the main selling points of vibe coding is that you don't necessarily need to be a professional to push useful products. That's a great marketing hook, but until we come close to AI superintelligence, you're going to need to know a thing or two.

In my experience, LLMs tend to lose focus with their builds; You'll often run into lines of code that don't perform any function or, worse, actively harm your codebase's complexity. How can you identify these problem lines if you don't know what each line is doing? For instance, I once built a machine learning model to predict the trend of stocks on the S&P 500. I had a working knowledge of Python and machine learning — I'd actively worked with the tech stack for about two years beforehand.

To save time, I vibe coded major parts of the project using ChatGPT, Claude, and Gemini. However, it became clear almost instantly that I'd have to be actively reviewing code for inefficiencies. The generated code would routinely explode my dataset sizes, which killed my notebook kernels more times than I care to admit. Fixing these issues was easy because, for the most part, I knew what I was doing. I could quickly sort out problems once they appeared. If you're going to vibe code, make sure you know enough to take the wheel once in a while.

It may be tempting to get fancy with your builds when vibe coding. After all, if you're using some of the best AI tools for coding, you're outputting code just as quickly as you can think. So, to take advantage of this technology, it's only natural to try to handle several tasks at once. However, it's vital to stay grounded and tackle one problem at a time — starting with the smallest possible issue.

Picture this for an example. Say you want to create a calorie-tracking app that comes with a leaderboard for comparing your activity with your friends. Maybe you want to include a feature for meal recommendations to make it fun. That's a lot of distinctly diverse features to handle at once. While LLMs will spit code to fit each one of your aspirations, chances are the generated code will not contain the level of detail required to scale — if it works at all.

Instead of putting all that diverse information into your chosen LLM at the same time, you're better off picking the simplest functionality and building it completely before moving on to the next hurdle. In the case of our offered example, the first item on the checklist should be the calorie tracking logic. Vibe coding is way easier with proper planning and execution, and the best way to get high-quality output is to zero in on one task at a time.

Using our last piece of advice as a springboard, the next item to discuss in the topic of vibe coding is prompt engineering. Not all prompts are created equal, and the quality of the code you'll get from an LLM depends on the context you provide. Lots of things go into crafting a good prompt; Proper layering into a mind map, breaking down complex tasks into simple ones, and even defining roles and boundaries. Across these factors, there's a constant that is essential to your vibe coding output: Specificity.

Being too general with your prompts is the start of a frustrating experience. Think of the LLM as an actual employee you're onboarding to your startup. You certainly wouldn't tell the new hire to build a calorie tracking app without intricate descriptions. Feed your chosen AI agent specifics of how you want your product to turn out; How you'd like users to interact with it, the brand identity you're trying to build, even down to minute details like design.

At the same time, while being specific, it would be wise to keep an eye on the number of tokens you're spending. The last thing you'd want is your chat limit warning popping up when you're not done with your project. So, manage your messages by placing as much context about a particular task in one text block. And remember, you can still Google basic stuff to save prompt credits.

LLMs are really helpful for thinking outside the box. They come up with ideas to solve problems and propose complementary features as you go on in your vibe coding journey. This ingenuity sounds like a dream, on paper. In reality, you could find yourself going down an AI-generated rabbit hole to deliver a niche feature nobody asked for. That wastes precious time and tokens. What you'll want to do instead, before even asking the LLM to write a single line of code, is to go into project management mode. Following the software development lifecycle procedure is just as essential to vibe coding as it is when dealing with real contractors.

The first step is planning. Discuss just about every detail you can think of with your AI of choice to develop a strong overarching structure that will guide your vibe coding experience. You can do this by asking the LLM to draw a solid plan, then fine-tune it to your needs. Once this part of the planning is complete, create an architectural framework for storing codes. It's a bit annoying, but it can save you a world of effort that's needed to tidy up a project when it inevitably gets too disorderly. Think of your role in vibe coding as that of a project manager; It may not be "vibes" after all, but it's an effective strategy that keeps your ideation-to-reality process a well-oiled machine.

Always test your code before merging any pull requests. We can't stress this point enough, so we're going to repeat it. No matter how careful and deliberate you've been with your prompting, some errors will find their way into an LLM's generated code. Your requirements may have been misinterpreted somewhere along the line, or logical errors could stop your code from running seamlessly. We have a whole list of the biggest problems with AI coding.

In light of this, exhaustive testing is paramount for keeping your app up and running. One of the worst places to be in as a vibe coder is when you can't identify which area of pushed code is causing your entire product to fail, and then you start crawling through a long history of prompts and responses to fish out the faulty component.

I've been there before, and it's not fun; I once spent the better part of three weeks progressively debugging a vibe coded application where I didn't bother with testing. So, while the speed of generative AI agents makes it tempting to power through the building process, you need to pump the brakes and review every proposed change. You could do this the modern way and have AI write test cases, but in my experience, a good old manual review of the code helps to ensure no security or performance risks slip through the cracks.