{"id":676,"date":"2024-08-29T11:45:00","date_gmt":"2024-08-29T09:45:00","guid":{"rendered":"https:\/\/itsonix.laolaweb.com\/?p=676"},"modified":"2026-05-13T11:58:51","modified_gmt":"2026-05-13T09:58:51","slug":"replace-me-if-you-can-2","status":"publish","type":"post","link":"https:\/\/itsonix.eu\/en\/blog\/replace-me-if-you-can-2\/","title":{"rendered":"Replace Me If You Can (2)"},"content":{"rendered":"

Last week, we showed how our design expert Tina fared in our little experiment. The goal was to develop a functional web application (mood tracker) for mobile use\u2014including the front end, back end, and design\u2014using AI tools. In this post, we\u2019ll take a look at the approach and results of our backend expert, Danny. We\u2019ll also examine the strengths and weaknesses of AI tools such as ChatGPT, Vercel v0, and GitHub Copilot Workspace.<\/p>\n\n\n\n

Backend Expert Becomes a Designer<\/h2>\n\n\n\n

Danny began developing the concept for the app using ChatGPT. He also utilized custom GPTs specifically tailored to user experience design to define the basic layouts and core functionalities of the mood-tracking app.<\/p>\n\n\n\n

v0<\/h3>\n\n\n\n

Once the concept was finalized, our backend expert enlisted the help of v0 for the design and frontend development. The AI tool v0 was developed by Vercel and is capable of generating executable front-end code from text and image descriptions. Danny fed the tool natural-language prompts that described various layouts and how they functioned.<\/p>\n\n\n\n

With additional input, Danny refined the target design until v0 reached its limits and even further iterations failed to deliver the desired results. The solution? GitHub Copilot Workspace.<\/p>\n\n\n\n

\"\" Iteration 1 (Source: v0<\/a>)<\/p>\n\n\n\n

\"\" Iteration 11 (Source: v0<\/a>)<\/p>\n\n\n\n

GitHub Copilot Workspace<\/h3>\n\n\n\n

GitHub Copilot Workspace is an AI-powered development environment. It enables developers to plan and implement projects\u2014from concept to completion\u2014using natural language. Compared to GitHub Copilot, the tool offers a task-oriented, collaborative environment. This enhances collaboration through real-time interaction with the AI, as well as specification-based planning and execution.<\/p>\n\n\n\n

Once v0 had reached its limits, our backend expert created a GitHub repository containing the code generated by v0 so we could continue development using Copilot Workspace. \u201eCertain customizations require direct intervention in the code, since the possibilities for creating prototypes with tools like v0 are eventually exhausted,\u201c says Danny. Since, as a backend expert, he has limited frontend knowledge, Copilot Workspace was particularly helpful to him. In particular, natural language programming made his frontend work considerably easier.<\/p>\n\n\n\n

To start a session with Copilot Workspace, first select the appropriate repository and then begin prompting. Start by defining a task and describing it in as clear terms as possible. After creating a well-structured task and the corresponding specification, Copilot Workspace provides suggestions for implementing the function in natural language. Based on the code from the repository and the task, Copilot Workspace\u2019s suggestions are usually high-quality, but you can still edit, expand, or add subpoints to them. This fosters collaboration between AI and developers and helps prevent misunderstandings during implementation.<\/p>\n\n\n\n

Our backend expert recommends keeping tasks small, even though the tool can handle larger tasks just fine. It\u2019s important to group related tasks together and avoid combining different tasks into a single request. A modular approach is the best way to maintain traceability. However, Copilot Workplace also has its limitations and makes mistakes that need to be corrected, which is why a basic technical understanding is required.<\/p>\n\n\n\n

While working on the backend, Danny implemented some things on his own, but he also relied more and more on ChatGPT and GitHub Copilot Workspace.<\/p>\n\n\n\n

\"\" Multiselect (Source: GitHub Copilot Workspace<\/a>)<\/p>\n\n\n\n

\"\" Multiselect Result (Source: GitHub Copilot Workspace<\/a>)<\/p>\n\n\n\n

During implementation, our backend expert encountered various challenges, particularly with the correct display of mood-tracking graphs (for example, v0 persistently used months instead of days). These were resolved through manual adjustments and iterative refinements in the Copilot Workspace. The final design improvements were ultimately achieved through targeted code adjustments in the GitHub Copilot Workspace.<\/p>\n\n\n\n

Despite the support provided by AI tools, technical expertise and manual adjustments were necessary to achieve a satisfactory result. By using ChatGPT, v0, and GitHub Copilot Workspace, our backend expert was able to create a functional and user-friendly interface, albeit with some limitations.<\/p>\n\n\n\n

By combining these tools with his own expertise and through multiple iterations, Danny was able to develop a mood-tracking journal that largely meets the product requirements that had been established earlier.<\/p>\n\n\n\n

\u201eUps & Downs\u201c Web Application\u201c<\/h2>\n\n\n\n

\"\" Home Page<\/p>\n\n\n\n

\"\" Statistics<\/p>\n\n\n\n

Conclusion and Outlook<\/h2>\n\n\n\n

The experiment clearly demonstrated the strengths of the various approaches: Tina\u2019s design stood out for its inviting layout and clear focus on user needs, while Danny\u2019s application impressed with its broader range of features. Both Xperts gained valuable insights into the potential and limitations of the AI tools they used.<\/p>\n\n\n\n

It became clear that AI solutions are not yet mature enough to fully replace human expertise. While these tools are particularly useful for user research, concept development, and branding, human validation and refinement of AI-generated results remain essential. AI tools were able to significantly speed up processes, particularly in rapid prototyping. However, technical understanding\u2014especially when it comes to coding\u2014remains necessary to successfully implement specific requirements. Nevertheless, it is reasonable to assume that AI tools will be increasingly integrated into design and coding processes in the future, as they noticeably improve both the quality and quantity of output in both areas.<\/p>\n\n\n\n

At this point, ethical questions quickly come into play. So far, AI-generated results have been based on extensive datasets that reflect the knowledge of many people. Time and again, certain professional groups have protested because their expertise has been\u2014and continues to be\u2014used to train machines without their consent or compensation. On the other hand, there is the question of to what extent AI should and is allowed to replace human labor. Various approaches exist in this area, such as Human-Centered AI, and there are also efforts at the legislative level to establish clear regulations. As we\u2019ve emphasized in previous blog posts and interviews with our Xperts, we want to highlight the positive benefits of AI. It\u2019s important to view the technology not as an adversary, but as a partner. No one can predict the future, yet we believe that human interaction and collaboration will continue to be important.<\/p>\n\n\n\n

Given the rapid advances in the field of AI, we plan to repeat this experiment on a regular basis and further strengthen interdisciplinary collaboration among our teams. To remain competitive, companies must prepare their employees for the future.<\/p>","protected":false},"excerpt":{"rendered":"

Letzte Woche haben wir gezeigt, wie sich unsere Design-Xpertin Tina in unserem kleinen Experiment geschlagen hat. Ziel war es, eine funktionsf\u00e4hige Webanwendung (Mood-Tracker) f\u00fcr mobile Zwecke inkl. Front- und Backend sowie Design mithilfe von KI-Tools zu entwickeln. Im aktuellen Beitrag widmen wir uns dem Vorgehen sowie dem Ergebnis unseres Backend-Xperten Danny. Dabei beleuchten wir die […]<\/p>\n","protected":false},"author":4,"featured_media":690,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[5],"tags":[],"class_list":["post-676","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-softwareentwicklung"],"acf":[],"yoast_head":"\nReplace me if you can (2) - IT Sonix - Ihr Partner f\u00fcr individuelle Softwarel\u00f6sungen<\/title>\n<meta name=\"description\" content=\"it Sonix Blog: Replace me if you can (2)\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/itsonix.eu\/en\/blog\/replace-me-if-you-can-2\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Replace me if you can (2) - IT Sonix - 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