What vibecoding actually means
Vibecoding is the practice of building software through iterative conversation with AI, where you describe intent in natural language and a model produces working code, then you steer, correct, and expand it in a fast feedback loop. The developer's primary act shifts from typing every line to directing the generation of lines and judging what comes back.
The term is often credited to the idea of "giving in to the vibes"—accepting AI output, running it, and iterating on feel rather than reading every character. In its purest, most caffeinated form, that is exactly what it is: you prompt, it builds, you run it, you react, you prompt again. For a weekend hackathon or a throwaway internal tool, that loop is a delight.
The trouble is that the word "vibe" implies you have switched off the part of your brain that asks inconvenient questions. And software, being the unforgiving medium it is, tends to ask those questions back—usually at 2 a.m., usually in production. So the honest definition has a second clause: vibecoding is iterative building with AI plus the judgment to know when the vibes are lying to you.
Where vibecoding earns its keep
There is a real category of work where vibecoding is not just acceptable but clearly the right tool. We reach for it constantly.
Prototyping is the obvious one. When the goal is to learn whether an idea is worth pursuing, the cost of being wrong is low and the cost of being slow is high. Generating three rough versions of a feature in the time it used to take to scaffold one is a straightforward win. You are buying information, not shipping a system of record.
Exploration is another. Unfamiliar library, new API, a corner of the language you have not visited since a bootcamp—an agent will produce a working example faster than you can find the right tab in the documentation. It is a tireless pair who has, apparently, read everything.
Then there is the unglamorous middle of the job: boilerplate, glue code, test scaffolding, one-off scripts, the migration you will run once and never think about again. This is the work that pays no intellectual dividends and consumes real hours. Handing it to a model is not cutting corners; it is refusing to spend a senior engineer's afternoon on typing.
Tools like Cursor's agent and coding assistants built on Claude have made this loop feel less like autocomplete and more like collaboration. You are no longer nudging a model one line at a time; you are handing it a task and reviewing the result. That is a meaningful difference in kind, not just degree.
Where the vibes run out
The failure mode is predictable, and we have cleaned it up more than once. Vibecoding produces code that works in the demo far more reliably than it produces code that works. Those are not the same sentence.
A model will happily generate a function that handles the happy path beautifully and quietly ignores the empty input, the network timeout, the concurrent write, and the malicious payload. It will invent a plausible API method that does not exist, wire up authentication that looks correct and leaks, or introduce a subtle data-shape mismatch that surfaces only when a customer in a different timezone does something reasonable. None of this is the model being stupid. It is the model doing exactly what it was asked—make it look right—without the accountability that makes a human engineer care whether it is right.
The other quiet cost is architectural. Vibecoded systems tend to accumulate what we think of as invisible debt: code that nobody read carefully, decisions nobody made deliberately, and a structure that emerged rather than was designed. It runs. It also becomes progressively harder to change, because no human holds a coherent model of how it fits together. Six weeks of pure vibes and you own a codebase you cannot confidently reason about—which, in engineering, is a polite word for a liability.
Our point of view at Chapter Two
We are enthusiastic about vibecoding and completely unsentimental about its limits. Here is how we hold both at once.
Vibecoding is a speed technique, not a trust technique. It changes how fast you can produce a candidate. It does nothing, on its own, to tell you whether that candidate is correct, secure, or maintainable. The discipline that establishes trust—tests, review, architecture, evaluation, human ownership—still has to be there. The AI moved the starting line; it did not move the finish line.
So the real question is never "should we vibecode or not." It is "what is this code for." For a prototype whose purpose is to be thrown away, lean all the way into the vibes. For anything that will hold real data, take real payments, or make real decisions, vibecoding is the first fifteen minutes of the job, not the whole job. The generated code is a strong first draft written by a fast, knowledgeable, slightly overconfident collaborator. You would not ship a junior engineer's first draft unreviewed; the same rule applies here, minus the mentoring relationship.
What This Looks Like in Practice
On our own work, a vibecoding session rarely looks like one heroic prompt. It looks like a conversation with guardrails.
We start by telling the agent what we are building and, crucially, what "done" means—the constraints, the edge cases we already know about, the parts that must not break. We let it generate. Then we read the output, because reading it is the entire point; the model's confidence is not evidence. We run it against real inputs, not just the ones that flatter it. Where it is clearly draft-quality, we iterate in place. Where it touches anything load-bearing, we treat the output as a proposal that has to earn its way in through tests and review.
For a genuine throwaway—an internal dashboard three people will use for a week—we happily skip most of that ceremony and let the vibes carry it. The judgment is in knowing which situation you are in before you start, not discovering it afterward. Frameworks and platform docs like the OpenAI and Gemini API guides are useful here precisely because they help you sanity-check what the model claims an API does versus what it actually does.
Common Mistakes
- Treating "it runs" as "it works." A green terminal is the beginning of verification, not the end of it.
- Shipping generated code you have not read. If no human understands it, no human can maintain it—or defend it when it fails.
- Vibecoding load-bearing systems on pure feel. Auth, payments, data integrity, and anything customer-facing deserve the full engineering treatment.
- Skipping the "what is this for" question. The same technique is brilliant for a prototype and reckless for a system of record. Decide up front.
- Assuming the model knows your context. It does not know your architecture, your constraints, or your history unless you tell it. It will confidently fill the gaps with plausible fiction.
Vibecoding is the entry point to AI-native development, not the destination. Once you accept that the vibes get you a fast first draft and no further, the next question becomes obvious: what separates a fun demo from software a business can actually depend on? That is not about writing cleverer prompts—it is about everything that surrounds the prompt. In the next article, we get into exactly that.
