How I Built a Prototype in a Week That Used to Take Months

In the world of development, where creating the first working prototype used to take months, modern tools and artificial intelligence now allow this period to be shortened to just one week. For me, as the founder of formspace.design, this is not just an acceleration, but a fundamental change in approach that allows hypotheses to be tested and ideas brought to life much faster, drawing on decades of experience in landscape design and business.

From Idea to First Click: Has the Development Approach Changed?

Recalling my first steps in business in the 90s, when I managed NPF "Alkar," setting tasks for programmers for management accounting was a real quest. We worked according to the classic scheme: a long technical specification, months of development, then testing and refinements. Each cycle took a very long time, and by the time the product was released, requirements might have already changed. My experience with "Dom i Sad" company, where I spent over 20 years on design and construction, only confirmed how critical it is to be able to quickly test ideas. Now, with the advent of advanced AI tools, I see how radically the process has changed. If before it was a dialogue with a programmer, now it's a dialogue with AI that understands my requests and generates code in real-time. I already discussed this in the article What has changed in 20 years: from ordering a technical specification from a programmer to a dialogue with AI.

Why Speed Matters: Lessons from the Past

My business journey taught me to value time and efficiency. At "Dom i Sad," we often faced situations where clients changed their preferences after work had already begun or even during the design process. Classic development, with its lengthy cycles, simply couldn't keep up with this dynamic. When it came to implementing CRM systems or accounting tools, processes dragged on for six months or more, and the cost of such projects reached tens of thousands of dollars. During this time, the market could change, and the product could become outdated even before launch. This led to customer loss and missed opportunities. I vividly remember how the lack of flexible accounting tools cost me considerable money and nerves. It was this experience that pushed me to create formspace.design – a tool that would allow for quick adaptation and response to market and client needs.

My New Approach to Prototyping with AI

Today, at 61, I am independently developing formspace.design using AI, and I don't have a staff of programmers. This has become possible thanks to a new approach to prototyping. My main principle is the minimum viable function (MVP). Instead of trying to create a perfect product from the first attempt, I focus on the most important function that will solve a specific problem. AI helps me quickly generate architecture, write code snippets, create user interfaces, and even test logic. I use it as a powerful assistant that significantly speeds up all stages of development. This allows me not only to reduce time but also to significantly lower risks, as I can quickly discard unviable ideas.

Steps to Building a Prototype in a Week

To build a working prototype in a week, I adhere to a clear plan:

1. Monday: Defining the Key Function and Architecture. I start by clearly formulating the task. What exactly should this prototype do? What single problem does it solve? Then, using AI, I sketch out a high-level architecture – what components are needed, how they will interact. For example, for a payment accounting module, I immediately define what data needs to be stored (amount, date, client, project).
2. Tuesday: Database and API Design. AI helps me design the database structure and define the main API methods. I literally describe what fields I need, and AI suggests an optimal schema and queries. This saves days of manual work. For client accounting, for example, these are fields for name, contact, associated projects.
3. Wednesday: Basic UI/UX Development. With the help of AI, I quickly create a basic user interface. I describe how the page should look, what controls are needed, and AI generates the frontend code. This is not necessarily the final design, but it is functional and allows interaction with the prototype. For example, a form for adding a new client or a table for viewing existing ones.
4. Thursday: Integration and Basic Logic. I connect the frontend with the backend, using AI-generated code to handle requests and interact with the database. At this stage, I also add basic business logic. For example, checking for mandatory fields when creating a record.
5. Friday: Testing and Refinement. At the end of the week, I conduct intensive testing of the prototype. AI helps me generate test scenarios and even find errors in the code. I check if the prototype performs its main function and make necessary adjustments. This allows me to identify critical errors before they become a big problem. I describe the prompt testing process in detail in the article Behind the Scenes: How I Test Prompts Before Embedding Them in a Product.

Key Principles of Effective Prototyping

Over the years, I have developed several principles that help me work effectively with AI and quickly create prototypes:

• Focus on one task: Don't try to solve all problems at once. Choose one, the most important function, and focus on it. This helps avoid distraction and overload.
• Minimalism: The prototype should be as simple as possible. Avoid unnecessary functions, complex design, and optimizations. The main thing is to test the idea, not to create a perfect product. The prototype can be very "raw," but it must work.
• Iterative: Be prepared for the first prototype not to be perfect. The goal is to get feedback and improve it in subsequent iterations. This is a continuous process of learning and development.
• Using ready-made solutions: Don't reinvent the wheel. Use ready-made libraries, frameworks, and, of course, AI capabilities to accelerate development. For example, if authorization is needed, I don't write it from scratch but use ready-made templates.
• Discipline: Working on a prototype requires strict discipline and adherence to the plan. Deviations from the schedule can drag the process on for weeks.

Common Mistakes in Rapid Development and How to Avoid Them

Rapid development, despite all its advantages, comes with certain risks. Here are some common mistakes I've encountered and how to avoid them:

• Over-engineering: The desire to build "for ages" even at the prototype stage. This leads to code complexity, slows down the process, and consumes resources. Solution: clearly separate the prototype and the final product. A prototype is a test, not a finished solution.
• Scope creep: Constantly adding new features during prototype development. "Let's add this too, and this would be nice." This kills the whole idea of rapid assembly. Solution: strictly adhere to the initially defined key function. Write down all new ideas for the future.
• Insufficient testing: The pursuit of speed can lead to a poorly tested prototype that doesn't work as intended. Solution: allocate enough time for testing at the end of the week. Let it be a simple but functional test.
• Ignoring feedback: Development for the sake of development, without considering the opinions of potential users. A prototype is needed for feedback. Solution: actively gather opinions and use them for subsequent iterations.

How I Apply Rapid Development Principles at formspace.design

When I build formspace.design, I'm not just creating a tool; I'm embedding into it the philosophy of rapid iteration and user experience that I've mastered myself. My goal is to empower you to "prototype" your landscape ideas as quickly as I prototype code.

For example, Module 2.1 "AI Concept" allows you, by uploading a photo of your plot or simply describing your wishes, to receive several layout options in a matter of minutes. Instead of spending hours on manual sketches or days waiting for a designer, you can instantly see how different zones might look: a relaxation area, a playground, a vegetable garden, or a decorative pond. This is your "quick prototype" of the landscape. You can compare 3-4 concepts, choose the one you like, and then work with it further, delving into details. Module 3.1 "Design by Photo" expands on this idea, allowing you to literally "try on" various landscape elements on real photos of your plot, whether it's new trees, flowerbeds, or gazebos. This gives you the ability to quickly assess how certain solutions will look on your property, even before you invest money and time in them.

FAQ

How has AI changed the development process for me?

AI has fundamentally changed my approach, transforming me from a task setter for programmers into an active developer. It has become my indispensable assistant, generating code, suggesting architectural solutions, helping with database design, and even testing. This allows me, a person without a classic programming education, to bring complex ideas into working prototypes in incredibly short periods, which was unthinkable just a few years ago.

What challenges arise with such rapid development?

The main challenges are related to the need to clearly formulate tasks for AI and not succumb to the temptation to "do everything at once." Sometimes AI can generate suboptimal or redundant code that requires refinement. It is also important to constantly check and test the generated solutions to avoid errors in the early stages. Maintaining focus and discipline are key challenges.

Does a prototype made in a week always become the final product?

No, not always. A prototype is a tool for testing a hypothesis, not necessarily the final product. Its main task is to demonstrate the viability of an idea, get feedback, and help make decisions about further development. Sometimes a prototype may be discarded if the idea proves unviable, or it may serve as the basis for a deeper and more thorough development of an already "clean" solution.

What is the main advantage of rapid iteration?

The main advantage of rapid iteration is the ability to learn and adapt quickly. The faster you can test an idea, the faster you learn what works and what doesn't. This minimizes risks, reduces development costs for unneeded features, and allows you to bring a truly necessary product to market faster, constantly improving it based on real user experience.

Can anyone start coding with AI?

I am confident that yes, given subject matter expertise and discipline. My own experience at 61 is proof of this. AI democratizes the development process, making it accessible to people who previously lacked programming skills. However, it is important to understand the subject area, be able to clearly formulate tasks, and have the desire to learn and experiment.

What tools, besides AI, do I use?

In addition to AI, I actively use modern frameworks and libraries for frontend and backend, version control systems (Git), and cloud platforms for deployment. AI becomes a powerful accelerator in working with these tools, helping me to master new technologies faster and apply them effectively. I also don't forget my experience with AutoCAD, Revit, and 3ds Max, which help me visualize and understand structure.

How do I check the quality of code created with AI?

I check the quality of AI-generated code through thorough testing, both manual and automated. I also perform refactoring to ensure the code is clean, readable, and adheres to best practices. AI can be very productive, but the human eye and experience are still necessary to ensure the reliability and maintainability of the code in the long term.

New Prototypes and Opportunities Ahead

My experience shows: what seemed impossible yesterday is becoming a reality today thanks to AI. The ability to assemble working prototypes in a matter of days opens up enormous prospects for the development of formspace.design and for me personally. This is a path to more flexible, adaptive, and truly useful products. And I continue on this path, creating tools that will help you bring your landscape dreams to life just as quickly and efficiently.

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