How does an idea evolve into a working prototype? Delivery Manager Andrey Sadakov and Product Owner Viktoriya Zinovyeva walk through the process in a recorded webinar and the text below. It’s a practical, step-by-step path from initial concept to prototype. They explain how to develop an MVP, utilizing effective coding practices that enhance development speed, recommend prototyping tools, and introduce semantic annotation.
Prototyping Video Demo
Watch the full webinar below or go through the article first.
Why AI Prototyping
Before AI-assisted development, execution carried most of the weight. Today, AI code generation has lowered that barrier.
AI helps us generate many ideas quickly, but human judgment still determines which directions are worth exploring.
AI speeds up development and improves quality, shifting the balance between ideation and execution; close to “implementation is nothing”.
Prototyping with AI streamlines discovery, requirements gathering, stakeholder alignment, and getting buy-in much faster. You can quickly build and show a prototype to evaluate potential early.
Product Idea
Imagine an app that helps diagnose pain and improve posture by analyzing user photos. It all started with Viktoriya's knee pain. A simple problem led to an idea: a posture analysis app.
Step 1: Shaping the Idea
The goal is to challenge the initial idea and generate alternatives using different perspectives.
Recommended Model: Use a reasoning-focused model for best results
Instruct the AI to:
Stay neutral and support recommendations with reasoning, pros, and cons for each option.
Ask about gaps or unclear points.
Present counterarguments when needed.
Provide the top three alternative strategies, including trade-offs.
List 5-7 questions to answer before moving forward.
Step 2: Market Research
This step identifies both supporting and opposing arguments for your chosen option, as well as the assumptions underlying them.
Recommended Model: Deep research mode
Include the results from step 1 for context. Then, instruct the LLM to:
Offer multiple viewpoints, and flag uncertainty levels where relevant.
Collect the strongest available evidence for and against each point.
Identify the five most critical assumptions.
This is the standard flow for market analysis: research competitors, look for signals from customer forums, social posts, and other customer insights. AI is suited to handle these exploratory tasks efficiently.
Step 3: Defining the Requirements
The goal is to design a clear specification for the AI prototyping tool.
Use the results from step 2 as input to give the full context. Ask for a Product Requirements Document (PRD) tailored to your specific tool (for example, Loveable). Adjust the PRD as needed, including elements such as personas, user stories, and functional requirements.
Specify the results format:
Use clear, simple language.
Cite pros and cons when suggesting alternatives.
Keep paragraphs short (under 4 lines).
Use bulleted lists where they clarify structure.
How a Prototype Differs from an MVP
| Aspect | Prototype | MVP (Minimum Viable Product) |
|---|---|---|
| Purpose | To visualize and test ideas quickly, often used for concept validation or stakeholder buy-in | To validate a product in the market with real users and gather feedback |
| Functionality | Simulated or partial functionality; may not be fully operational | Core, working functionality that delivers actual value to users |
| Goal | Demonstrate feasibility, design, or workflow | Test market demand, usability, and product-market fit |
| Testing | Usability tests, concept validation, internal review | Live testing with users, real-world data, and customer feedback |
| Risk Mitigation | Reduces design/technical risks early | Reduces market and business risks |
| Iteration Speed | Very fast, since it's not fully functional | Slower than prototypes, but still faster than a full-scale product |
| Outcome | Helps decide whether to move forward with building an MVP | Provides insights for scaling or pivoting to a better product version |
Technical Comparison: Prototype vs MVP
| Aspect | Prototype | MVP (Minimum Viable Product) |
|---|---|---|
| Code Quality | Often quick and dirty, may use throwaway code, mockups, or scripts. Not built to scale or maintain. | Production-grade (even if minimal). Uses maintainable code that can be extended later. |
| Backend | Usually faked (e.g., mocked API responses, static JSON files, or stubs). | Real backend services, even if basic—often with database, APIs, and authentication. |
| Frontend | May be static screens or interactive mockups with limited or no logic. | Fully functional UI connected to backend, handling state and real interactions. |
| Deployment | Usually not deployed; runs locally, in a demo environment, or as design mockups. | Deployed in production (cloud, app stores, or web), accessible by real users. |
| Longevity | Disposable—meant to validate ideas before coding seriously. | Foundation for future development—can evolve into the whole product. |
| Code Quality | Usually faked (e.g., mocked API responses, static JSON file, or stubs). | Production-grade (even if minimal). Uses maintainable code that can be extended later. |
Vibe Coding Best Practices
Build iteratively. Don’t try to generate an app from a single prompt. Start simple and build up in steps.
Choose a standard tech stack. LLMs can work with many frameworks, but JavaScript frameworks such as ReactJS, as well as Java or C#, are more reliable.
Provide context. Share relevant documentation, app functionality, business goals, and technical details. The more context the AI has, the better the results will be. Protocols like MCP help keep documentation up to date.
Set coding rules. Coding standards vary by language (e.g., JavaScript, NodeJS). Add these rules to prompts to help the AI produce better code.
Use semantic annotation.
Semantic Annotation
Just as we comment on code and update documentation for others, we use the same principle for AI. Create a readme.md file in markdown to explain your app’s business logic. Add clear comments in the code to describe its purpose, inputs, outputs, and side effects. You can also use XML to annotate the code. This helps AI understand the code's intent, not just how to run it. Use semantic markup to make this easier.
Recommended Tools
Loveable: A platform for quickly turning concepts into web applications. Best for creating clickable prototypes
Replit: An online IDE with AI features. Balances automation with developer control. Suitable for both speed and functionality.
Local Setup: Use Cursor with the Caluse Sonnet model for a hands-on, developer-driven approach. Best for full-scale development.
What to Do Next
With a clear structure and the right tools, anyone can create a prototype. Good documentation and small, well-defined steps make it easier to turn an initial idea into functional products quickly and easily. Start iterating and see where your ideas lead!
*The article was initially published on DataArt Team blog.
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