The operations manager at a metalworking company in Valencia has had the same problem for months: his team records production incidents in a shared spreadsheet that keeps corrupting, that nobody updates consistently, and that produces no useful reports. Commissioning a custom tool from a software provider means months of waiting and a budget that has not been approved. A year ago, there was no alternative. Today he describes what he needs in plain language and has a working prototype by Friday.<\/p>\n
That is Vibe Coding. This article explains exactly what it is, how the real workflow operates, how it differs from existing alternatives, which tools make it possible, which use cases make most sense for an industrial company in Spain and, above all, what concrete risks exist when it is applied without adequate technical supervision.<\/p>\n
The term was coined in February 2025 by Andrej Karpathy<\/a>, co-founder of OpenAI and former Director of AI at Tesla. He described it as a style of programming where the developer “fully gives in to the vibes, embraces the exponentials and forgets that the code exists”. Behind the casual framing lies a technically precise idea: instead of writing code, you describe in plain language what you want the software to do, and an AI tool generates it.<\/p>\n Collins Dictionary named “Vibe Coding” its Word of the Year 2025<\/a>. In 2026, 41% of all code written worldwide is now generated by AI<\/a>. This is not an emerging trend: it is the new normal of software development.<\/p>\n Vibe Coding is the practice of creating functional software using natural language instructions, without manually writing code. The AI interprets what is described, generates the corresponding code and the result is tested directly. If something does not work as expected, the adjustment is described. The cycle repeats until the result is correct.<\/strong><\/p>\n What can be built with this process is concrete:<\/p>\n If it can be described clearly, AI can attempt to build it.<\/p>\n The starting point is not the tool: it is clarity about what is needed. An effective prompt is not “build an app for the company”. It is: “I want a web tool where the production team can log incidents with date, shift, defect type and an attached photo, and where the quality manager can view a dashboard with the last 30 days of records filtered by defect type.” The more precision in the input, the more useful the output.<\/p>\n With the prompt defined, the tool generates the code and, in many cases, deploys the application in a test environment accessible from the browser. That the app works technically does not mean it solves the problem it was created for. That judgement is exclusively human, and it determines the real value of the output.<\/p>\n If something is wrong, the problem is described in plain language and the AI corrects it. That iteration cycle is fast. But there is a point at which informal iteration is no longer sufficient: when the tool will handle real customer or employee data, when it will integrate with existing systems, or when it will be used in production continuously. At that point, technical review by a specialist turns the prototype into a sustainable business asset.<\/p>\nThe definition that works for someone who is not a programmer<\/h3>\n
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How Vibe Coding works in practice: the four-step cycle<\/h2>\n
Step 1 \u2014 Describe: turning a business need into a precise prompt<\/h3>\n
Step 2 \u2014 Generate and test: the AI builds, the team evaluates<\/h3>\n
Steps 3 and 4 \u2014 Iterate and decide when to involve an expert<\/h3>\n
Vibe Coding vs. No-Code vs. Low-Code vs. traditional programming<\/h2>\n
The comparison table every manager should read before making a decision<\/h3>\n<\/p>\n