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The most common mistake made by vibe coding beginners is mistaking AI for a fortune-teller that knows and decides everything on its own. A single word thrown out without context, like “Make a login feature,” has a high probability of leading the project to ruin. This is because AI spits out just “the most common and plausible code” without knowing the user’s environment, security level, or tech stack. When assigning work to AI, you must deliver clear “constraints” and “context”—not vague “feelings”—so you don’t fail.
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Mason: A novice developer who tries to clear a massive feature with a single line of instruction, believing “AI is smart, so it will figure everything out on its own.”
Sophia: A 10-year veteran developer who warns of the terrible future brought by security-flawed code and emphasizes that clear guidelines must be provided to AI.
Daniel: A level-headed project manager with a planning background who has witnessed countless times how vague instructions tangle up development schedules and communication.
Q. Why Will Asking AI to “Make a Login Feature” End in Disaster in vibe coding?
Mason tapped on his monitor and said in an annoyed tone:
“I mean, I told the AI very intuitively, ‘Make a login feature,’ right? So it generated a bunch of code, but when I pasted it into our existing app screen, over a hundred errors popped up. Plus, nothing happens when I click the login button. Is the AI failing to understand what I’m saying, or did it just write half-baked code?”
Sophia walked over to Mason’s seat, looked at the single-line question written in the prompt window, and chuckled.
“Mason, if you throw a request at AI like that, unless it’s a god, how would it know our system’s setup? Since you gave it no information, the AI just loosely combined ‘the most common sample login code’ floating around the internet sea and threw it at you.”
Mason argued defensively:
“But ‘login’ is a set thing! You enter an ID, enter a password, and if they match, you get in!”
Daniel calmly stepped in while looking at his documents.
“Mason, even from my perspective as a planner, there are dozens of types of login. You didn’t give any context on whether our service is a web or mobile app, whether we use cookies or sessions, whether we use JWT tokens, or whether it’s Kakao or Google social login. Since the tech stacks don’t align, of course it’s bound to blow up with errors.”
The Limit of Probabilistic Generative Models: An Answer Without Context Is a Wrong Answer
An AI (LLM) is not an inference engine that finds correct answers with perfect logic. It is a probabilistic model that connects ‘the most plausible text with the highest probability of coming next’ based on the text previously entered. Therefore, if you throw out a massive requirement while omitting context, it is guaranteed to fail for the following reasons:
- Tech Stack Mismatch: If our project uses React and Next.js, AI might spit out login code based on pure HTML and Express if there’s no mention of it. Copying and pasting this code directly breaks the build for the entire project.
- Missing Security Filters: Half-baked login code highly likely lacks input validation or carries critical security flaws, such as transmitting passwords as plain text without encryption. This happens because AI prioritizes suggesting ‘the most commonly used code that just runs’ over ‘strictly secure code.’
- Architectural Breakdown: It might also produce outputs where hundreds of lines of code are crammed into a single file, completely ignoring the folder structure or data flow maintained by the existing project.
Sophia took Mason’s mouse and opened a new prompt window.
“When handling AI, you have to be a meticulous and demanding senior developer. Instead of ‘Make a login feature,’ you should bundle our current situation and constraints clearly before throwing the request. Try writing it like this.”
Sophia typed on the keyboard to draft a new prompt:
[Request Prompt]
We are currently building a web service using Next.js 14 (App Router) and Tailwind CSS. We are using SQLite for our database.
[Implementation Requirements: Login Feature]
- Frontend: Cleanly design an input form component that takes an email and password using Tailwind.
- Validation: Validate on the client side first whether the email format is correct and whether the password is at least 8 characters long.
- Backend: Utilize Next.js Server Actions to write logic comparing the user table and password in the database.
- For security, passwords must be encrypted using the bcrypt library before comparison.
- If authentication succeeds, issue a mock JWT token and print it to the console.
Do not modify other existing files; separate and write only the code for the newly added component file and server action file.
The moment she hit enter, the AI began printing out login code with a precise architecture that perfectly matched the current project—neatly separated by file—completely different from the previous useless code.
Mason gulped as he watched the screen.
“Wow… because the question was specific, it accurately tells me where the code should go in the folders and even the terminal commands to install necessary libraries. It has zero errors and wires up immediately.”
Daniel smiled and said:
“That’s exactly why the resolution of your question is so important in vibe coding. AI is not a fortune-teller; it’s a giant mirror. If we throw a blurry and vague question, it reflects blurry junk code; if we throw sharp and precise constraints, it reflects sharp, premium code to match. Ultimately, to make AI work well, humans must first clearly know and instruct what is needed.”
Mason opened his notebook and summarized today’s failure and lesson in one line: “Do not throw requests at AI using vague nouns; instruct with sentences containing constraints and context.” Learning how to turn vague feelings into sharp logic, Mason’s prompts finally began to gain real power that wouldn’t fail.
Chapter 6 Summary
- Asking AI for an abstract feature (e.g., “Make a login feature”) without context causes the system to break down because it spits out “the most common sample code” that doesn’t fit your project’s tech stack or architecture at all.
- Without specific instructions, AI easily suggests dangerous code lacking security validation or exception handling, so you must explicitly state input validation and encryption methods.
- For successful vibe coding, you must include explicit contexts and constraints in your prompt: [Tech Stack, Framework Version, Database Structure, Architecture Rules].