ਵਾਈਬ ਕੋਡਿੰਗ ਕੀ ਹੈ?

Vibe coding is the practice of building software by describing what you want in plain, natural language and letting an AI model generate the code, rather than writing it line by line yourself. The person prompts, the AI generates, and the person tests and iterates on the result, often without reading every line the model produces.

That’s the short answer to “what is vibe coding.” The fuller vibe coding meaning, and where the term actually came from, is worth walking through, because it’s shifted since it was coined.

Where the Term Came From #

Vibe coding has a specific origin: a single social media post. On February 2, 2025, AI researcher and OpenAI co-founder Andrej Karpathy posted on X describing a new way he’d started building software. He said he’d “fully give in to the vibes” and let large language models write the implementation while he focused on describing outcomes rather than syntax.

Karpathy described the workflow in practical terms: he talked to an AI coding assistant by voice, accepted its suggestions without reading the diffs, and pasted error messages back in without comment until they resolved themselves. He later summarized the loop even more simply: <cite index=”8-1″>”I just see stuff, say stuff, run stuff, and copy-paste stuff.”</cite>

The post spread fast. Within weeks it was covered by major outlets, and by March 2025, Merriam-Webster had added “vibe coding” to its dictionary as a tracked slang and trending term, defining it as <cite index=”29-1″>”writing computer code in a somewhat careless fashion, with AI assistance.”</cite> By the end of 2025, Collins Dictionary had gone further and named it its Word of the Year.

How the Meaning Has Shifted #

Here’s where “vibe coding meaning” gets more interesting than the origin story alone. Karpathy originally used the term to describe his own low-stakes, personal way of building throwaway prototypes, not a methodology he was proposing for production software. But the phrase escaped that context almost immediately.

Today, vibe coding meaning has broadened to cover a wide range of AI-assisted development, from hobbyists building a weekend app to startups shipping production software this way. <cite index=”20-1″>About 63% of vibe coding users identify as non-developers, including product managers, marketing directors, and designers</cite>, and <cite index=”20-1″>roughly a quarter of Y Combinator’s Winter 2025 startup cohort reported codebases that were 95% or more AI-generated</cite>. Analysts expect that trend to keep accelerating: <cite index=”20-1″>Gartner projects citizen developers will outnumber professional engineers four to one by 2028</cite>.

How Vibe Coding Actually Works? #

Most descriptions of the vibe coding workflow break down into a repeatable loop:

1. ਤੁਰੰਤ। Describe the desired outcome in natural language, not code. (“Build a dashboard that shows monthly revenue by customer segment, with a date filter and CSV export.”)
2. ਪੈਦਾ ਕਰੋ। The AI model produces code, sometimes a full file, sometimes a diff against existing code.
3. Run and observe. The person runs the result and checks whether it behaves as described, rather than reading the implementation line by line.
4. ਦੁਹਰਾਓ। Follow-up prompts refine the result. Errors are often pasted back to the model as-is rather than debugged manually.

The defining characteristic isn’t that AI is involved (autocomplete and copilots have used AI for years). It’s the degree of disengagement from the code itself: the person is evaluating outcomes, not scrutinizing implementation.

Vibe Coding vs. Traditional AI-Assisted Coding #

It’s worth distinguishing vibe coding from other AI-assisted workflows, since the terms get used loosely:

• Autocomplete/copilot tools suggest code as you type, but a developer is still writing and reviewing each line.
• ਏਆਈ-ਸਹਾਇਤਾ ਪ੍ਰਾਪਤ ਕੋਡਿੰਗ more broadly can include any workflow where AI drafts code that a developer then reviews and edits with full understanding.
• ਵਾਈਬ ਕੋਡਿੰਗ specifically implies the human is guiding by outcome, through prompting, testing, and iteration, rather than by reading and understanding the generated implementation.

That distinction matters, because it’s the “not reading the diffs” part, not the AI itself, that creates most of the risk discussed below.

The Risk Side of Vibe Coding #

This is a glossary entry, not a warning label, but no definition of vibe coding is complete without the security research that’s accumulated around it, because it’s become one of the most measured downsides of the practice. Multiple independent studies converge on a similar range: <cite index=”18-1″>a December 2025 study of open-source repositories found that AI-generated code introduced security vulnerabilities in 45% of development tasks</cite>, and <cite index=”18-1″>AI-assisted pull requests generated roughly 2.74 times more security issues than human-authored code in the same comparison</cite>. Specific vulnerability classes show up disproportionately: <cite index=”24-1″>one study found XSS vulnerabilities in 86% of AI-generated code samples tested across five major language models</cite>.

Dependency hallucination is a related, well-documented pattern. <cite index=”19-1″>A Cloud Security Alliance research note from April 2026 found that across 2.23 million AI-generated code samples from sixteen models, 19.7% contained at least one hallucinated package name that doesn’t actually exist</cite>, and <cite index=”19-1″>43% of those hallucinated names reappeared consistently every time the same prompt was repeated</cite>, which is what makes the pattern exploitable rather than random. Independent researchers have also started formally tracking the downstream effect: <cite index=”21-1″>Georgia Tech’s Vibe Security Radar recorded 35 new CVE entries directly attributed to AI-generated code in March 2026 alone, up from six in January</cite>.

None of this means vibe coding is unusable, the productivity gains are real and adoption isn’t reversing. It does mean that “the AI wrote it and it ran” and “it’s safe to ship” are two different claims, and treating AI-generated code with the same scrutiny as an unreviewed third-party contribution is quickly becoming standard advice across the security research on this topic.

ਕੀ ਟੇਕਵੇਅਜ਼ #

• Vibe coding is a specific term, not a synonym for “AI-assisted coding.” It specifically describes building by outcome and iteration rather than by reading and understanding the generated code.
• It was coined by Andrej Karpathy on February 2, 2025, and reached Merriam-Webster’s slang list within five weeks.
• Vibe coding meaning has broadened from a personal, low-stakes practice to a mainstream development approach used by both hobbyists and funded startups.
• The risk isn’t AI-generated code itself, it’s unreviewed AI-generated code. Independent research consistently finds elevated vulnerability rates and predictable dependency hallucination in vibe-coded output.

Vibe-coded software still has to ship through the same pipeline as everything else. ਜ਼ਾਇਗੇਨੀ ਦਾ platform applies the same SAST, SCAਹੈ, ਅਤੇ AI-aware analysis ਨੂੰ AI-generated code as it does to human-written code, ਸਮੇਤ ਮਾਲਵੇਅਰ ਸ਼ੁਰੂਆਤੀ ਚੇਤਾਵਨੀ (MEW) for the kind of hallucinated-dependency risk described above, so a fast prompt-to-production workflow doesn’t quietly skip the review step.

ਸਵਾਲ #