Using Loveable the Right Way: A Founder's Checklist

Loveable is incredible for getting something built fast. You describe what you want, and working code appears. For founders who’ve been stuck in the planning phase, that feels like a superpower.

But speed without structure creates a different kind of problem. You ship in weeks, then spend months untangling what you shipped. The UI looks right. The demo works. Then a real user hits an edge case and the whole thing falls over. You open the codebase to fix it and realize you don’t understand half of what’s in there.

We’ve helped multiple founders recover from exactly this situation. Some needed a complete rebuild. Others caught the problems early enough to course-correct. The difference almost always comes down to how they used the tool — not whether they used it.

This post is the checklist we wish every founder had before they started building with Loveable. Seven rules. All practical. All learned from real projects.

Loveable is a tool, not an engineering team

Let’s be clear: we’re not anti-AI. We’re not anti-Loveable. We use AI coding tools ourselves — Cursor, Bolt, and yes, Loveable too. These tools are genuinely good at certain things.

Loveable is strong at prototyping. It generates clean UI components quickly. It can scaffold a multi-page app in minutes. For getting an idea out of your head and onto a screen, it’s hard to beat.

But it’s a code generator, not an architect. It doesn’t understand your business logic. It doesn’t know that your user model needs to handle three different permission levels. It doesn’t think about what happens when your database has 50,000 rows instead of 50. It doesn’t plan for the integration you’ll need next month.

Every prompt gets a fresh context. Loveable doesn’t maintain a holistic view of your application. It solves the immediate request. Sometimes that solution conflicts with something it generated two prompts ago. Sometimes it creates a new data model when it should’ve extended an existing one. Sometimes it duplicates logic rather than abstracting it.

None of this makes Loveable bad. It makes it a tool with specific strengths and specific limits. The founders who get good results from Loveable understand both. They use it for what it’s good at and don’t ask it to do what it can’t.

Knowing the difference between a code generator and an engineering team is the difference between a prototype that becomes a product and a prototype that becomes a liability.

Seven rules for building with Loveable

These aren’t theoretical. They come from real projects we’ve seen — the ones that worked and the ones that didn’t.

1. Write an instructions.md first

Before you generate a single line of code, write a document that describes your application’s architecture. What’s the tech stack? What are the main entities? How do they relate to each other? What are the core user flows?

Loveable supports project-level context. Use it. Create an instructions.md file that tells Loveable about your conventions, your folder structure, your naming patterns. Think of it as onboarding a new developer. The more context you provide upfront, the more consistent the output will be.

Without this, every prompt starts from zero. Loveable will make reasonable-sounding decisions that contradict each other across prompts. You’ll end up with three different ways to handle API calls, two different state management patterns, and component names that follow no consistent logic.

Fifteen minutes writing instructions saves you hours of cleanup later.

2. Design your database schema before generating UI

This is the single most impactful rule on this list.

If you don’t define your data model upfront, Loveable will create data structures on the fly. Each prompt gets whatever schema seemed reasonable at that moment. You’ll end up with a users table, a profiles table, a user_data table, and an accounts table — all storing overlapping information with no clear relationships between them.

Sit down and sketch your schema before you open Loveable. What are the core entities? What are the relationships? What fields does each entity need? Write it out. Put it in your instructions.md. Reference it in your prompts.

Your data model is the foundation of your application. Get it right first. Let Loveable build on top of it, not around it.

3. Keep prompts small and focused

“Build me a dashboard with user management, analytics, and billing” is a terrible prompt. Not because Loveable can’t attempt it, but because the output will be a monolithic chunk of code that’s hard to review, hard to modify, and hard to debug.

One feature per prompt. One concern per prompt.

“Create a user list component that displays name, email, and role.” That’s a good prompt. You can review the output, verify it works, and move on. If something’s wrong, you know exactly where the problem is.

Small prompts also give you natural checkpoints. You review after each one. You catch issues early. You don’t build three features on top of a broken foundation.

Think of it like commits in version control. Small, focused, reviewable.

4. Review every output before moving on

This sounds obvious. Almost nobody does it.

The temptation is real: Loveable generates something, it looks right in the preview, you move on to the next prompt. You’re building fast. You’re in the zone. Stopping to read the code feels like it’s slowing you down.

It’s not slowing you down. It’s the only thing keeping you from building a house of cards.

Read the generated code. Understand what it’s doing. Check that it’s using the data model you defined. Verify it’s not duplicating logic that already exists. Make sure it’s not introducing patterns that conflict with what you’ve already built.

If you can’t understand the code Loveable generated, that’s a red flag. Either simplify your prompt or get someone who can review it. Code you don’t understand is code you can’t maintain.

5. Don’t let Loveable handle auth or payments

Authentication and payment processing are the two areas where mistakes cost the most. A bug in your dashboard layout is annoying. A bug in your auth system is a security breach. A bug in your payment flow is lost revenue and potentially a legal problem.

Loveable will happily generate auth code if you ask. It might even look reasonable. But AI-generated auth has a pattern of subtle, dangerous flaws. Passwords stored incorrectly. Session tokens that don’t expire. Permission checks that can be bypassed. These aren’t bugs you’ll catch in a demo.

Use established libraries. Supabase Auth, Clerk, Auth0 — pick one and integrate it properly. For payments, use Stripe’s official SDK. These are solved problems with battle-tested solutions. Don’t let a code generator reinvent them.

The same principle applies to any security-sensitive feature: data encryption, file upload validation, API rate limiting. Use proven tools for critical infrastructure.

6. Add tests from day one

“I’ll add tests later” means “I’ll never add tests.” And with AI-generated code, tests matter more, not less.

Here’s why: Loveable doesn’t refactor. When you add a new feature, it doesn’t go back and update related code to maintain consistency. It generates new code that works with the current prompt. Sometimes that breaks something that was working before. Without tests, you won’t know until a user finds it.

You don’t need comprehensive test coverage on day one. Start with the basics. Does the login flow work? Does creating a new record save to the database? Do the critical user paths complete without errors?

Even a handful of integration tests give you a safety net. Every time Loveable generates new code, run your tests. If something breaks, you catch it immediately instead of discovering it three weeks and twenty prompts later.

7. Plan your exit from day one

This isn’t about abandoning Loveable. It’s about making sure you can maintain your codebase without it.

Structure your code so a human developer can understand it. Keep a clean folder structure. Use consistent naming. Document the non-obvious decisions. If Loveable generates something with an unusual pattern, either refactor it to match your conventions or add a comment explaining why it’s different.

The goal is simple: six months from now, when you hire your first developer or bring in a team like ours, they should be able to open the codebase and understand what’s happening. If the only way to make changes is to keep prompting Loveable, you don’t own your codebase — Loveable does.

This also means version control. Commit after every meaningful change. Write descriptive commit messages. If a Loveable generation goes wrong, you need to be able to roll back to a known-good state.

The cost of skipping these rules

We’re not sharing these rules to be cautious for caution’s sake. We’ve seen what happens when they’re skipped.

One founder came to us after building a SaaS platform with Loveable over six weeks. The app had four different user models — users, profiles, accounts, and members — created across different prompts over different sessions. Each one stored slightly different data. Some features read from users, others from profiles. There was no single source of truth for who a user was or what they could access. Consolidating those into a single coherent model cost $12,000 and took three weeks. The original build had taken less time.

Another founder asked Loveable to handle authentication. The generated code stored passwords as plain text in the database. No hashing. No salting. Passwords sitting there, readable by anyone with database access. The founder didn’t know enough to catch it during review. A security-conscious friend spotted it two months later. The fix — proper auth implementation, password migration, security audit, notifying affected users — cost $8,000 and a lot of trust.

A third founder built an impressive-looking product in six weeks. Shipped it. Got initial users. Then the bug reports started. Each fix introduced new problems because the codebase had no consistent architecture. No tests to catch regressions. No clear data model to reason about. That founder spent three months debugging before calling us. At that point, rebuilding from scratch was faster than untangling what existed.

These aren’t worst-case scenarios. They’re common outcomes. The pattern repeats: fast initial build, impressive demo, then a slow unraveling as real usage exposes the structural problems that vibe coding left behind.

When to bring in help

You don’t need a full engineering team from day one. Loveable and similar tools genuinely do reduce how much professional engineering time you need in the early stages.

But there are clear signals that it’s time to bring in experienced engineers.

You have paying users. Real users depending on your product changes the stakes. Bugs aren’t just annoying anymore — they cost you customers. When revenue is on the line, your codebase needs to be stable and maintainable.

You’re about to raise. Technical due diligence is real. Investors will ask about your tech stack, your architecture, your ability to scale. “I built it with Loveable” isn’t disqualifying, but “I built it with Loveable and nobody’s reviewed the code” is a red flag.

Bugs are outpacing features. If you’re spending more time fixing things than building things, the codebase has accumulated too much structural debt. More prompting won’t fix this. It usually makes it worse.

You need integrations. Connecting to third-party APIs, handling webhooks, managing background jobs — these are areas where generated code often falls short. The edge cases matter and they’re hard to express in a prompt.

At any of these stages, bringing in professional help for an MVP development engagement or a codebase review can save you months. Not because AI tools are bad, but because there’s a point where human judgment and architectural thinking become necessary.

Build it right the first time

Loveable can absolutely be part of your stack. The founders who get the most from it are the ones who treat it as a powerful assistant, not a replacement for engineering thinking.

Use the checklist. Write your instructions. Design your schema. Review every output. Keep the critical stuff in proven libraries. Test as you go. And plan for the day when you or someone on your team needs to maintain the code without AI help.

If you’re about to start building and want to set things up correctly from the beginning, or if you’ve already built something and aren’t sure about its foundation, get in touch. A short conversation now can save you a long, expensive cleanup later.

Building with Loveable? Variant Systems helps founders use AI tools effectively without creating technical debt.