Realistic Vibe Coding: SEO Examples That Work
▼ Summary
– Vibe coding is a method of creating software by using AI tools like ChatGPT to generate code from plain language prompts, eliminating the need to write code manually.
– It is best suited for prototypes, internal tools, or tasks where the outcome does not require exact precision, rather than for building complex commercial applications.
– The basic process involves writing a prompt, pasting the generated code into an environment like Google Colab, and then running and testing the results.
– Users must carefully validate AI outputs for errors or fabricated information and be explicit about their platform and requirements in prompts.
– Real-world examples demonstrate vibe coding can create tools like Chrome extensions, Google Sheets automations, and simple games, expanding possibilities for non-developers.
Vibe coding represents a significant shift in how software is created, allowing individuals to generate functional code through simple conversational prompts directed at AI tools. This approach, which Collins Dictionary recognized as the word of the year for 2025, enables the construction of programs without writing a single line of code manually. By describing a desired outcome in plain language to platforms like ChatGPT or Gemini, users receive executable code they can run in environments such as Google Colab. For SEO professionals and marketers, this opens up exciting possibilities for building custom tools, automating workflows, and rapidly prototyping ideas that were previously gatekept by technical complexity.
The core process involves three straightforward steps. First, you craft a detailed prompt outlining exactly what you want the code to accomplish. Next, you paste the AI-generated code into a runtime environment. Finally, you execute the code and analyze the results. The barrier to entry is remarkably low, often requiring just a free ChatGPT account and access to Google services. However, it’s crucial to manage expectations. Vibe coding excels at producing outcomes for specific data tasks where precision isn’t absolute, such as finding related links or adding tags to content. It is ideal for demos, internal applications, or minimum viable products (MVPs). For building commercial software intended for sale, traditional AI-assisted coding with tools like GitHub Copilot, which demands deeper programming knowledge, is a more viable path.
Success with this method hinges on writing explicit prompts. Clearly state the platform, like Google Colab, and specify all input and output formats. If the code fails due to missing packages, you can simply paste the error message back into ChatGPT for a corrected version. A critical caveat is that AI can sometimes generate convincingly wrong or entirely fabricated outputs, so rigorously validating results is non-negotiable. When integrating paid APIs from services like Semrush or OpenAI, remember to use your own API keys and monitor associated usage costs.
Beyond basic scripts, the SEO community has leveraged vibe coding to create sophisticated tools. For instance, one professional built a Chrome extension that performs sentiment analysis on Google Business Profile reviews, using Claude for prompt engineering and Cursor for code generation. Another created a Google Sheets tracker that monitors Knowledge Graph confidence scores for entities via a free API. In a more creative application, a link-building asset was developed in the form of a browser game where players quickly accept or reject email pitches, built entirely with guidance from ChatGPT using HTML, CSS, and JavaScript.
These examples illustrate the practical potential, but they also reveal common challenges. As projects grow in complexity, AI can struggle with context, sometimes requiring users to start fresh conversations to maintain effectiveness. Iteration and patience are often necessary to refine the final product.
Ultimately, vibe coding is a powerful tool for augmentation, not replacement. It responsibly unlocks new ways for SEOs to prototype ideas, automate repetitive tasks, and explore creative experiments without a heavy technical lift. The key to success is a realistic understanding of its best uses: where “good enough” results are acceptable, outputs are diligently checked, and projects are scaled up with additional resources when they outgrow the prototype stage. This approach fosters faster testing, sharper insights, and a greater capacity for innovation, pairing creative curiosity with practical restraint.
(Source: Search Engine Land)
