Vibe Coding: The Future of AI-Powered Software Development

The Advent of Vibe Coding

The rapid development of technology over the past three decades has created a revolution, transforming the world through innovations across various industries. Today, we’re on the cusp of another transformation in this digital age — a transformation heralded by AI.

AI has always had a niche in computing, mainly as a research subject, but technological innovations have skyrocketed in recent years. These innovations have greatly impacted industries such as medicine and business, leading to the rise of Generative AI, or GenAI.

Simply put, GenAI uses generative models to produce text, images, videos, or other forms of data. These models learn their training data’s underlying patterns and structures and use them to create new data based on the input, usually in natural language prompts. Increased usage of GenAI tools like ChatGPT, Gemini, Grok, Midjourney, and Sora has led to the streamlining and automation of various tasks while raising some ethical dilemmas that have yet to be resolved.

What is Vibe Coding

In software development, using text-based chatbots trained using large language models (LLMs) has resulted in a recent surge of AI-generated code based on an input prompt. This practice would involve the developer (or a user of some kind) to engineer a text-based prompt that can be communicated to the chatbot to generate code based on the specifications mentioned in the prompt. The accuracy of the code generated is dependent on the details included in the input prompt. Andrej Karpathy, a co-founder of OpenAI, coined this practice as “Vibe Coding,” referring to it as a coding approach that relies on LLMs to generate working code by providing natural language descriptions rather than manually writing it.

Since its coinage, vibe coding has been the subject of memes and running jokes in developer communities like Reddit and LinkedIn, viewing it as a fad rather than a viable development approach. The consensus in these communities is that vibe coding leads to a lack of accountability, and heavy reliance on unreliable AI-generated code can potentially cause security vulnerabilities in the long run. While vibe coding presents many pitfalls, this approach shows great promise in improving development times and making it more accessible.

The Benefits of Vibe Coding

In essence, vibe coding is an extension of pair programming where the LLM can function as a pseudo-driver who generates the code (which can be inferred as identical to writing the code). The programmer can act as an observer who helps navigate the AI to create the code that matches the required specifications. The programmer can design or engineer a core system effortlessly by engaging in a back-and-forth conversation with the AI. This approach encourages a “design-first” problem-solving that is becoming increasingly essential. Microsoft’s CEO, Satya Nadella, had stated in an interview that “all of us are going to be software architects,”¹ when asked about the AI-driven software landscape of today. This aligns with the notion that software development has evolved into a problem-solving endeavour rather than simply writing lines of code.

Beyond system design, vibe coding can be a companion tool for perusing documentation. A lot of documentation these days can either be overwhelming or underwhelming – neither of which enables the programmer to understand the related software agilely. LLMs can alleviate this problem by only showing documentation about the prompt. I recently worked on a logging framework and had to investigate whether Graylog, a log management tool, could integrate with our Spring applications. By passing a simple prompt to ChatGPT, I was able to view some documentation related to a GELF appender (short for Graylog Extended Format – basically the format in which Graylog captures its logs, which is why it requires an appender to translate it accordingly) that can help in the integration process. While the appender’s official documentation lacked any practical examples, GPT provided me with some useful snippets that enabled me to understand how Graylog worked under the hood with ease and use this learning to implement it for our project.

Finally, vibe coding makes software development more accessible and approachable. Now, amateur developers don’t face ridicule from snarky StackOverflow comments and responses and can rely on prompts to get solutions to their problems from any LLM. The data also supports developers moving away from forums like StackOverflow, thanks to Vibe Coding. Based on data collected in December 2024, since ChatGPT launched in November 2022, StackOverflow had 82,997 fewer questions, a 76.5% decline². The reason for this decline is simple – LLMs like GPT are not hostile towards developers asking genuine questions, thus contributing significantly to the rise of vibe coding.

The Challenges of Vibe Coding

While vibe coding has its advantages, knowing its many pitfalls is essential. As mentioned above, developers are wary of vibe coding as a practical development approach due to the lack of accountability and potential security vulnerabilities. Given that many LLMs are trained on outdated data (ChatGPT, for example, was trained on data up until June 2024), some of the information it generates can be out-of-date, which is why it is important to cross-check the generated information with that found online or in documentation. This verification is much more critical when working with emerging tools or software since they can undergo constant updates quickly.

Likewise, LLMs generate code based on the context provided to them by the programmer, and in most cases, the entire context will not be provided. This results in the LLM generating code based on an incomplete context. If the programmer happens to rely on these tools overly and unthinkingly copy the generated code into their workspace without verification, the latent errors stemming from the change could become a debugging nightmare for them – something that LLMs are currently not capable of (some of them can list out possible causes of the issue, but it does this by attempting to narrow down the causes, which is very tedious). A preliminary study at MIT also suggests that overreliance on LLMs could lead to cognitive debt³. As such, we must remain vigilant about our dependence on LLMs as an always-be-all and end-all solution. My recommendation with vibe coding would be first to ask the AI to generate a skeletal framework based on a generic prompt, then continue to communicate with it to refine it based on the requirements.

The Future of Vibe Coding

Despite this, vibe coding is an exciting approach and a natural evolution of the software development process. As AI grows and LLMs expand their functionalities, vibe coding will play an essential role in software engineering to drastically improve development times. Through vibe coding, software development will become more systems design-focused. It will make it more accessible for developers to learn and understand new technologies faster in a more streamlined manner. At Covalense Digital, our development teams have already embraced vibe coding as a solid approach to simplify our system architecture.

Ready to harness the power of vibe coding and AI-driven development for your next project? Our AI/ML expert team can help you integrate these cutting-edge approaches into your development workflow. Contact Us to discuss your requirements and discover how we can transform your software development process.

Author

Varun Peesapati, Senior Research Engineer - I
Varun is a coding enthusiast with a natural zeal for learning about new technologies, especially the latest innovations in AI. He is also an avid reader and a hobbyist science fiction writer.