Vibe Coding SaaS MVPs: The Ultimate Guide

The complete guide to building SaaS MVPs using vibe coding methodology for rapid development.

the future of work, simply

• work is shifting from clicking to orchestrating. we’ll spend less time doing and more time deciding what should be done.
• agents won’t replace judgment. they’ll make room for it. the value moves to problem framing, taste, and systems thinking.
• small teams will feel bigger. an operator with good prompts and clean processes becomes a force multiplier.
• the winners will be boring on purpose. simple, reliable systems beat flashy tools. consistency compounds.

if you’re overloaded and want space to think again, we can start small. map the flow, remove the drags, and let agents handle the busywork. keep what matters human.

Prompt Pack 2.0 polished and ready to paste

Kickoff: clarify the idea

Create a PRD from the discussion

Tech stack evaluation

Project setup in Cursor

Generate the Home screen component

Core recommendation flow

Backend API endpoint (filter + simple ranking)

Replace mock data with Google Places later

QA bug report and fix loop

Performance pass on long lists

Deployment prep and checklist

part one define your idea in simple words

just start small. write what you want to build in one or two lines. who is it for, what problem it fixes, and what “done” looks like. keep it chill and honest.

• what is the app for
• who is going to use it
• what is the one thing it must do well for v1
• how will someone use it from start to finish

here’s a tiny prompt you can paste to your ai buddy to talk it through

when the chat feels clear, ask for a mini prd (nothing fancy)

if you want a quick visual, this is the flow

pro tips (soft and simple)

• save your mini prd. you’ll reuse it a lot
• keep scope tiny now. add nice-to-haves later
• if you feel stuck, write the flow as a story “a user opens the app, does x, sees y”

part two choose a simple stack that won’t fight you

keep it easy. pick tools that are common and have lots of examples. you can always swap later.

• one-shot route: lovable, bolt, or replit builds most of it for you fast
• guided route: cursor or windsurf helps you build piece by piece

how to decide fast

• if the app is simple and you just want something working today → one-shot
• if you want control and to learn the code → guided

recommended defaults

• web: next.js + typescript + tailwind
• data: supabase or firebase
• auth: the one built into your data choice
• deploy: vercel

paste this to your ai if you want a quick pick

quick visual

pro tips

• choose boring, popular tech. it’s easier to debug
• don’t chase perfect. ship the mvp, then improve
• write down any trade-offs so future you remembers

part three scaffold the app fast with ai

goal get the skeleton running so you can click around. no polish. just a working base.

if you use onedshot

• create a project in lovable, bolt, or replit
• paste your mini prd and any mustdhave tech choices
• wait a few minutes, then run it and click everything

if you use guided (my usual)

• open cursor
• ask it to set up next.js + typescript + tailwind + api routes
• let it create folders, configs, and scripts
• run the dev server and make sure it boots

copydpaste prompt

what to check

• can you start it locally without errors
• does the home page render
• do you see the folders and basic files you expect

visual

pro tips

• if something fails, paste the exact error into the chat
• keep commits small "setup ok", "home page ok"
• move on once it runs dondt chase details yet

part four shape the ui in small, clear pieces

think lego. one block at a time. get the screen to show up, then add little details.

start with the home screen

• title and a short line that says what this does
• one big button that starts the main action
• an empty state area that says “nothing here yet”
• simple nav at the bottom or top

copy‑paste prompt

add more screens the same way

• list page
• details page
• a small form for inputs

visual

pro tips

• make it work on mobile first. simple layouts scale up easily
• name components clearly so you can find them later
• after each screen renders, commit and move on

part five make the app actually do the thing

now we wire buttons to real work. keep it tiny and test as you go.

what this means

• when a user clicks a button, something happens
• we read inputs, run a function, and show a result

start with the smallest path

• take the form values
• call a simple function recommend()
• show a loading state, then results, or a friendly error

copy‑paste prompt

visual

pro tips

• test with fake data first
• always show empty and error states it feels polished
• commit after it works once end‑to‑end

part six connect data without stress

we’re adding real data, but keep it calm. start fake, then go real.

start with mock data

• make a data/restaurants.json with ~20 items
• build a tiny api route that reads from it and filters

then add a real source later (like google places)

• add env vars
• fetch on the server
• map their fields to your fields
• keep a fallback to the mock json if the api fails

copy‑paste prompt

when you’re ready to switch to google places

visual

pro tips

• keep secrets in .env, never in code
• start simple, then harden with auth and logs later
• test with empty, small, and large results

part seven test it, fix it, ship it

keep this super light. test like a user, then fix the rough edges.

basic loop

• try each flow end‑to‑end
• when you see something weird, write one short note
• fix one thing at a time, then retest

quick prompts you can paste

visual

ship with confidence

• run your smoke test
• deploy to vercel
• click through the main flow in prod
• jot down any follow‑ups for later

pro tips

• tiny fixes ship faster than big refactors
• write down the exact steps to reproduce a bug
• if performance feels slow, try loading fewer items on screen first

that’s it

start small, keep it simple, ship early. then improve.

Supercharging Your Prompts

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

• 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”
• Provide examples – When available, show the AI examples of similar features or styling you like
• Establish context – Remind the AI of previous decisions or the broader architecture
• Request explanations – Ask the AI to explain its approach before implementing
• 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:

• Chunk large codebases – Most AI tools have context limitations. Focus on specific files or components rather than the entire application at once.
• Verify third-party interactions – Double-check code that integrates with external APIs or services, as AI may generate outdated or incorrect integration code.
• Beware of hallucinations – AI might reference nonexistent functions or libraries. Always verify dependencies and imports.
• Plan for maintenance – Document AI-generated code thoroughly to make future maintenance easier.
• 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:

• Schedule refactoring sessions – After implementing features, dedicate time to clean up and optimize code.
• Use AI for code review – Ask your AI assistant to analyze your codebase for duplications, inefficiencies, or potential bugs.
• Document architectural decisions – Record why certain approaches were chosen to inform future development.
• Implement automated testing – Even simple tests can catch regressions when making changes.
• 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