Vibe Coding SaaS MVPs: The Ultimate Guide

I’ll be honest with you, I don’t actually like the term “vibe coding” because it makes it sound easy and error-free. Like oh I’m just going with the vibes, let’s see where it takes us.

But the reality is it takes a lot of back and forth, restarts, research, repeats, re-everything to build and ship a functional MVP, even with AI. It’s fun, but it can get frustrating at times. And it’s in those moments of frustration where most people give up.

I’m going to help you break through those moments of frustration so that you can come out successful on the other side.

The approach I outline here isn’t theoretical. It’s a process that I’ve refined after countless hours using these tools and developing and shipping functional apps like Content Spark, a video analysis tool, and many more. Follow it and you’ll be shipping products in no time.

PS – If you want to know what exactly vibe coding is, read my introductory article here.

PPS – If you like videos, watch my process here –

Step 1: Define Your App Concept and Requirements

Before jumping into any coding tools, you need a clear vision of what you’re building. The quality of AI-generated code depends heavily on how well you communicate your idea. Even a simple one-paragraph description will help, but more detail leads to better results.

First, create a basic description of your app in a document or text file. Include:

• The app’s purpose (what problem does it solve?)
• Target users
• Core features and functionality
• Basic user flow (how will people use it?)

Then, use an AI assistant to refine your concept. Gemini 2.5 Pro is my favorite model right now, but you can use any other reasoning model like Claude 3.7 Sonnet Thinking or ChatGPT o3. Paste your description in, and ask it to help you flesh out the idea.

I'm planning to build [your app idea]. Help me flesh this out by asking questions. Let's go back and forth to clarify the requirements, then create a detailed PRD (Product Requirements Document).

Answer the AI’s questions about features, user flows, and functionality. This conversation will help refine your vision.

Request a formal PRD once the discussion is complete.

Based on our discussion, please create a comprehensive PRD for this app with:
1. Core features and user flows
2. Key screens/components
3. Data requirements
4. Technology considerations
5. MVP scope vs future enhancements

Let's discuss and refine this together before finalizing the document.

In the video, I’m building a restaurant recommendation app. I started with a simple description and Gemini broke this down into manageable pieces and helped scope an MVP focused on just Vancouver restaurants first, with a simple recommendation engine based on mood matching.

Pro Tips:

• Save this PRD—you’ll use it throughout the development process
• Be specific about what features you want in the MVP (minimum viable product) versus future versions
• Let the AI suggest simplifications if your initial scope is too ambitious

Step 2: Choose Your Tech Stack

Now that you have a clear plan, it’s time to decide how you’ll build your app and which AI tools you’ll use. Decide between these two main approaches :

a) One-shot generation: Having an AI tool generate the entire app at once

• Best for: Simple apps, MVPs, rapid prototyping
• Tools: Lovable.dev, Bolt.new, Replit, Google’s Firebase Studio
• Advantages: Fastest path to a working app, minimal setup

b) Guided development: Building the app piece by piece with AI assistance

• Best for: More complex apps, learning the code, greater customization
• Tools: Cursor, Windsurf, VS Code with GitHub Copilot, Gemini 2.5 Pro + manual coding
• Advantages: More control, easier debugging, better understanding of the code

In my video, I demonstrated both approaches:

• One-shot generation with Lovable, which created a complete app from my PRD
• Guided development with Cursor, where I built the app component by component

For the rest of this guide, I’ll continue to explain both approaches although I do think the guided development provides an excellent balance of control and AI assistance.

For one-shot approach, you simply need to sign up to one of Lovable, Bolt, or Replit (or try all three!). For guided, there are a couple of extra steps:

• Install Cursor (cursor.sh) or your preferred AI-assisted IDE
• Set up a local development environment (Node.js, Git, etc.)
• Connect your GitHub account for version control
• Create an account on Vercel for easy deployment

Finally, decide on:

• Frontend framework (e.g., React, Vue, Svelte)
• Backend approach (e.g., Node.js, serverless functions)
• Database needs (e.g., Firebase, Supabase, MongoDB)
• Any third-party APIs or services

If you need help, ask your AI assistant to recommend appropriate technologies based on your requirements and to evaluate trade-offs.

Example Prompt:

For the restaurant recommendation app we've described, what tech stack would you recommend? I want something that:
1. Allows for rapid development
2. Has good AI tool support
3. Can scale reasonably well if needed
4. Isn't overly complex for an MVP

For each recommendation, please explain your reasoning and highlight any potential limitations.

Pro Tip: Mainstream technologies like React, Next.js, and common databases generally work better with AI tools because they’re well-represented in training data.

Step 3: Generate the Initial App Structure with AI

Now it’s time to create the foundation of your application.

If Using One-Shot Generation (Lovable, Bolt, etc.):

1. Create a new project in your chosen platform
2. Paste your PRD from Step 1 into the prompt field
3. Add any clarifications or specific tech requirements, such as: Please create this app using React for frontend and Supabase for the backend database. Include user authentication and a clean, minimalist UI.
4. Generate the app (this may take 5-10 minutes depending on complexity)
5. Explore the generated codebase to understand its structure and functionality

If Using Guided Development (Cursor):

1. Open Cursor and create a new project folder
2. Start a new conversation with Cursor’s AI assistant by pressing Ctrl+L (or Cmd+L on Mac)
3. Request project setup with a prompt like:

Let's start building our restaurant recommendation app. First, I need you to:

1. Create a new Next.js project with TypeScript support
2. Set up a basic structure for pages, components, and API routes
3. Configure Tailwind CSS for styling
4. Initialize a Git repository

Before writing any code, please explain what you plan to do, then proceed step by step.

In the video, I asked Cursor to “create a React Native expo app for a restaurant recommendation system” based on the PRD. It:

• Created app directories (components, screens, constants, hooks)
• Set up configuration files
• Initialized TypeScript
• Created placeholder screens for the restaurant app

With Lovable, I simply pasted the PRD and it generated the complete app structure in minutes.

In both cases, I’m just asking the agent to build everything from the PRD. However, in reality, I prefer to set it up myself and build the app by generating it component by component or page by page. That way, I know exactly what’s happening and where the different functions and components are, instead of trying to figure it out later.

Pro Tips:

• When using Cursor, you can execute terminal commands within the chat interface
• For React or Next.js apps, setup typically involves running commands like npx create-react-app or npx create-next-app (which Cursor can do for you)
• Check that everything works by running the app immediately after setup
• If you encounter errors, provide them to the AI for troubleshooting

Step 4: Build the User Interface With AI

Now that you have the basic structure in place, it’s time to create the user interface for your app.

If Using One-Shot Generation:

1. Explore the generated UI to understand what’s already been created
2. Identify changes or improvements you want to make
3. Use the platform’s chat interface to request specific changes, like:

The home screen looks good, but I'd like to make these changes:
1. Change the color scheme to blue and white
2. Make the search button more prominent
3. Add a filter section below the search bar

If Using Guided Development:

Create your main UI components one by one. For a typical app, you might need:

• A home/landing page
• Navigation structure (sidebar, navbar, or tabs)
• List/grid views for data
• Detail pages or modals
• Forms for user input

For each component, prompt Cursor with specific requests like:

Now I need to create the home screen for our restaurant recommendation app. It should include:

1. A welcoming header with app name
2. A prominent "Find Recommendations" button
3. A section showing recent recommendations (empty state initially)
4. A bottom navigation bar with icons for Home, Favorites, and Profile

Please generate the React component for this screen using Tailwind CSS for styling. Focus on clean, responsive design.

I’ll also use Gemini 2.5 Pro from the Gemini app in parallel. I’ll continue the same chat I started to write the PRD and have it strategize and build the app with me. Gemini tends to be more educational by explaining exactly what it is doing and why, allowing me to understand how this app is being built.

Pro Tips:

• For web apps, ensure your components are responsive for different devices
• Start with simple layouts and add visual polish later
• If you have design inspiration, describe it or provide links to similar UIs
• For mobile apps, remember to account for different screen sizes
• Test each screen after implementation to catch styling issues early

Step 5: Implement Core Functionality

With the UI in place, it’s time to add the functionality that makes your app actually work.

If Using One-Shot Generation:

1. Test the functionality that was automatically generated
2. Identify missing or incorrect functionality
3. Request specific functional improvements through the chat interface:

I notice that when filtering restaurants by mood, it's not working correctly. Can you modify the filtering function to properly match user mood selections with restaurant descriptions?

If Using Guided Development:

1. Implement core functions one by one. For example, in a restaurant app:
   • Search/filter functionality
   • Data fetching from API or database
   • User preference saving
   • Authentication (if needed)
2. For each function, provide a clear prompt to Cursor:

Let's implement the core recommendation feature. When a user clicks "Find Recommendations," they should see a screen that:

1. Asks for their current mood (dropdown with options like "romantic," "casual," "energetic")
2. Lets them select cuisine preferences (multi-select)
3. Allows setting a price range (slider with $ to $$$$ options)
4. Has a "Show Recommendations" button

When they click the button, it should call our recommendation function (which we'll implement later) and show a loading state.

Please write the React component for this feature, including state management and form handling.

Pro Tips:

• For backend-heavy functionality, consider using Firebase, Supabase, or other backend-as-a-service options for simplicity
• Implement one logical piece at a time and test before moving on
• When errors occur, copy the exact error message and provide it to the AI
• Break complex functions into smaller, more manageable pieces
• Use comments in your prompts to explain the expected behavior in detail

Step 6: Add Backend and Data Management

Most apps need data. Whether you’re using mock data, a database, or external APIs, this step connects your app to its data sources.

If Using One-Shot Generation:

1. Check what data sources were set up automatically
2. Request database or API integration if needed
3. Provide necessary API keys or connection strings as instructed
4. Test the data integration thoroughly

I want to replace the mock restaurant data with real data from the Google Places API. Please update the app to: 1. Connect to the Google Places API 2. Fetch nearby restaurants based on user location 3. Store favorites in a database (like Firebase or Supabase)

If Using Guided Development:

1. Define your data models and database schema
2. Implement API routes or serverless functions
3. Connect frontend components to backend services
4. Add authentication if required

Example Prompt:

For our restaurant recommendation app, I need to create the data layer. Let's:

1. Define a Restaurant data model with fields for name, cuisine types, price range, location, and a text description
2. Create an API endpoint that returns restaurants filtered by the user's preferences
3. Implement a simple algorithm that matches restaurants to the user's mood based on keywords in the description
4. For now, use a JSON file with 20 sample restaurants as our data source

Please implement this backend functionality in our Next.js API routes.

Pro Tips:

• Test with various data scenarios (empty results, large result sets, etc.)
• Start with mock data until your UI works correctly
• For external APIs, paste their documentation into the chat to help the AI generate correct integration code
• When using databases, start with a simple schema and expand as needed
• Keep API keys and sensitive credentials out of your code (use environment variables)

Step 7: Test, Debug, and Refine

The final step is to thoroughly test your application, fix any issues, and deploy it for others to use.

If Using One-Shot Generation:

1. Test all user flows in the generated app
2. Report and fix any bugs through the platform’s interface
3. Deploy your app using the platform’s deployment options
4. Share your app with testers or users to gather feedback

I found these issues while testing: 
1. The app crashes when submitting an empty search 
2. Restaurant images don't load correctly 
3. The back button doesn't work on the details screen 
Please fix these issues.

If Using Guided Development:

1. Conduct systematic testing of all features:
   • Basic functionality testing
   • Edge case testing (empty states, error handling)
   • Performance testing
   • Device/browser compatibility testing
2. Fix bugs with AI assistance: I'm encountering this error when trying to submit the search form: [paste error message] Here's the code for the search component: [paste relevant code] Please help identify and fix this issue.
3. Optimize performance if needed: The restaurant list is loading slowly when there are many results. Can you suggest ways to optimize this component for better performance?
4. Prepare for deployment: Help me prepare this app for deployment. I want to: 1. Set up production environment variables 2. Optimize the build for production 3. Deploy the frontend to Vercel and the backend to Render Please provide the necessary steps and configurations.
5. Deploy and monitor your application

In the video demonstration, we encountered and fixed several issues:

1. A 404 error due to mismatched API endpoints
2. Authentication token issues with the OpenAI API
3. UI rendering problems on the restaurant listing screen

This is bound to happen, especially if you’re trying to build something more complex than a landing page. We fixed these by examining error messages, updating code, and testing incrementally until everything worked correctly.

Most importantly, don’t give up. If you’re stuck somewhere and AI can’t help you figure it out, Google it, or ask a friend.

I've found a bug in our recommendation feature. When a user selects multiple cuisine types, the filtering doesn't work correctly. Here's the error I'm seeing:

[Paste error message or describe the issue]

Here's the current code for the recommendation function:

[Paste the relevant code]

Please analyze the issue and suggest a fix.

Pro Tips:

• Always test thoroughly after making significant changes
• Keep your browser console open to catch JavaScript errors
• Use Git commits after each successful feature implementation
• Document any workarounds or special configurations you needed
• Create multiple small commits rather than one large one
• If the AI makes changes that break functionality, you can easily revert to a working state.

Advanced Techniques for Power Users

Supercharging Your Prompts

The quality of your prompts directly impacts the quality of AI-generated code. Use these techniques to get better results:

1. Be specific and detailed – Instead of “create a login form,” specify “create a login form with email and password fields, validation, error handling, and a ‘forgot password’ link”
2. Provide examples – When available, show the AI examples of similar features or styling you like
3. Establish context – Remind the AI of previous decisions or the broader architecture
4. Request explanations – Ask the AI to explain its approach before implementing
5. Break complex requests into steps – For intricate features, outline the steps and have the AI tackle them sequentially

Handling AI Limitations

Even the best AI assistants have limitations. Here’s how to navigate them:

1. Chunk large codebases – Most AI tools have context limitations. Focus on specific files or components rather than the entire application at once.
2. Verify third-party interactions – Double-check code that integrates with external APIs or services, as AI may generate outdated or incorrect integration code.
3. Beware of hallucinations – AI might reference nonexistent functions or libraries. Always verify dependencies and imports.
4. Plan for maintenance – Document AI-generated code thoroughly to make future maintenance easier.
5. Establish guardrails – Use linters, type checking, and automated tests to catch issues in AI-generated code.

Managing Technical Debt

Rapid development can lead to technical debt. Here’s how to minimize it:

1. Schedule refactoring sessions – After implementing features, dedicate time to clean up and optimize code.
2. Use AI for code review – Ask your AI assistant to analyze your codebase for duplications, inefficiencies, or potential bugs.
3. Document architectural decisions – Record why certain approaches were chosen to inform future development.
4. Implement automated testing – Even simple tests can catch regressions when making changes.
5. Monitor performance metrics – Track key indicators like load time and memory usage to identify optimizations.

Building a Restaurant Recommendation App with AI

Let’s walk through how this process worked for building the restaurant recommendation app shown in my video:

Initial Concept and Requirements

I started with a basic idea: an app that recommends restaurants based on a user’s mood and preferences. Using Gemini 2.5 Pro, I fleshed out this concept into a detailed PRD that included:

• Core features: mood-based filtering, restaurant browsing, favorites
• User flows: search, view details, save favorites
• Data requirements: restaurant information, user preferences
• MVP scope: focus on just restaurants first, with basic mood matching

Development Approach and Implementation

I demonstrated both approaches:

With Lovable (One-Shot Generation):

• Pasted the PRD into Lovable
• Generated a complete app in minutes
• Explored the generated code and UI
• Found it had created:
  • A clean, functional UI
  • Mock restaurant data
  • Basic filtering functionality
  • Simple “vibe matching” based on keyword matching

With Cursor (Guided Development):

• Set up a React Native project using Expo
• Created individual components for screens and functionality
• Built a backend with Express.js
• Implemented “vibe matching” using OpenAI
• Connected everything with proper API calls
• Fixed issues as they arose through debugging

Challenges and Solutions

Both approaches encountered issues:

• Endpoint mismatches between frontend and backend (fixed by aligning route paths)
• API key configuration (resolved by setting proper environment variables)
• Data sourcing (initially used mock data, with plans to integrate Google Maps API)

The Result

Within our session, we successfully built:

• A functional restaurant recommendation app
• The ability to filter restaurants by mood, cuisine, and price
• A simple but effective “vibe matching” algorithm
• A clean, intuitive user interface

The entire process took less than an hour of active development time, demonstrating the power of AI-assisted coding for rapid application development.

Best Practices and Lessons Learned

After dozens of projects built with AI assistance, here are the key lessons and best practices I’ve discovered:

Planning and Architecture

1. Invest in clear requirements – The time spent defining what you want to build pays dividends in AI output quality.
2. Start simple, add complexity gradually – Begin with a minimal working version before adding advanced features.
3. Choose proven technologies – Stick to widely-used frameworks and libraries for better AI support.
4. Break down large features – Decompose complex functionality into smaller, manageable pieces.

Working With AI

1. Test after every significant change – Don’t wait until you’ve implemented multiple features to test.
2. Don’t blindly accept AI suggestions – Always review and understand what the AI is proposing.
3. Be specific in your requests – Vague prompts lead to vague results.
4. Keep track of the bigger picture – It’s easy to get lost in details; periodically step back and ensure alignment with your overall vision.
5. Use version control religiously – Commit frequently and create checkpoints before major changes.

Code Quality and Maintenance

1. Document as you go – Add comments and documentation during development, not as an afterthought.
2. Implement basic testing – Even simple tests help catch regressions.
3. Refactor regularly – Schedule time to clean up and optimize AI-generated code.
4. Maintain consistent patterns – Establish coding conventions and ensure AI follows them.
5. Prioritize security – Verify authentication, data validation, and other security practices in AI-generated code.

Conclusion: The Future of Development

We’re experiencing a profound transformation in how software is created. AI code-generation and tools built on them are changing who can build applications and how quickly ideas can be turned into working software.

This doesn’t mean traditional development skills are becoming obsolete. Rather, the focus is shifting from syntax mastery to system design, user experience, creative problem-solving, and effective AI collaboration. The most successful developers in this new landscape will be those who can clearly articulate their intent and effectively guide AI tools while maintaining a strong foundation in software engineering principles.

As you embark on your own vibe coding journey, remember that AI is a powerful collaborator but not a replacement for human judgment. Your creativity, critical thinking, and domain expertise remain essential. The tools will continue to evolve rapidly, but the process outlined in this guide (defining clear requirements, building incrementally, testing rigorously, and refining continually) will serve you well regardless of which specific AI assistants you use.

Now it’s your turn to build something amazing. Start small, embrace the iterative process, and watch your ideas come to life faster than you ever thought possible.