Making an AI-Generated Web Map with Lovable
The SparkGeo team has tested Lovable and asked it to create a simple interactive map showing Canadian cities and their population.
results weren’t always consistent. Running the same prompt twice sometimes yielded drastically different outcomes: one time clean and functional, the next time oddly styled or partially broken. In one case, even after explicitly stating not to use any frameworks, Lovable still generated a React-based implementation. A quick follow-up prompt corrected this, and to its credit, the revised output was clean, readable vanilla JavaScript.
That responsiveness is promising, but also highlights an important caveat: some knowledge of development (especially in geospatial UI) is still necessary. Without it, spotting issues or debugging quirks could become a frustrating barrier. Lovable gets you 80% of the way there, but that final 20% – the part where accuracy, accessibility, and usability matter most, still depends on human expertise.
Even for simple applications, you need a knowledgeable engineer to create a finished product. This is fine if you treat generative AI assistants as a tool, not the solution.
But an important question lingers. While engineers rely more and more on generative AI to build applications, they spend less time understanding the concepts and libraries they’re working with, making mistakes and gaining experience allowing them to understand and improve the code they write. By extension, engineers never learn enough about the technology they’re putting to work, maybe even unlearn some of the things they know now. If that turns out to be the case, who will maintain applications largely built with AI in the future?