Lovable is extremely good at building. Give it an idea, and it often returns something far more complete than you expected—authentication, dashboards, settings, edge cases, and polished UI included. While this feels impressive, it can quietly work against one of the core goals of an MVP: learning fast with minimal effort.
Overbuilding doesn’t happen because Lovable is “too smart.” It happens because AI optimizes for completeness unless you tell it not to. Understanding why this happens—and how to control it—can help you build leaner, faster, and smarter MVPs.
Why Lovable Naturally Overbuilds
Lovable is trained to be helpful. When you ask for a feature, it assumes you want something production-like and robust. It fills in gaps, adds structure, and anticipates needs you didn’t explicitly request.
This leads to:
- Extra screens you didn’t ask for
- Full auth flows when a mock login would do
- Complex data models instead of flat structures
- Polished UI when wireframes would be enough
- Edge-case handling that slows iteration
Lovable isn’t wrong—it’s just solving a different problem than MVP validation.
The Hidden Cost of Overbuilding an MVP
An MVP isn’t about completeness. It’s about speed of feedback.
Overbuilding creates several risks:
- Longer build times
- Harder pivots
- More code to maintain
- False confidence from polished output
- Delayed learning
When an MVP feels “done,” teams are less likely to change direction—even if the idea is wrong.
Why AI Amplifies This Problem
Human developers naturally push back when asked to overbuild. AI doesn’t. It executes eagerly and optimizes for what it thinks is “best.”
Without constraints, AI builders like Lovable will:
- Assume scale matters
- Assume real users exist
- Assume production readiness is required
This is great for real products—but dangerous for experiments.
How to Stop Lovable From Overbuilding
The solution isn’t to use Lovable less. It’s to use it more precisely.
1. State MVP Constraints Explicitly
Tell Lovable upfront:
“This is an MVP. Prioritize speed over completeness. Skip edge cases, auth, and polish unless requested.”
This single instruction changes everything.
2. Ask for Skeletons, Not Systems
Use prompts like:
“Create a basic flow with placeholder data.”
“Mock the backend, don’t build it.”
“Wire the UI only—no real logic yet.”
Lovable responds well to scope boundaries.
3. Limit the Surface Area
Build one flow at a time. Avoid prompts that describe the full product vision. Focus on the smallest possible test.
4. Defer Real Infrastructure
Use fake auth, static data, or in-memory state early. Replace it later once the idea proves itself.
5. Review and Remove
Lovable often adds helpful extras. Don’t be afraid to delete them. Removing code is faster than maintaining it.
When Overbuilding Is Actually Useful
There are times when Lovable’s tendency to overbuild is an advantage:
- Internal tools
- Investor demos
- One-off client projects
- Fast-follow products
- Teams validating execution, not ideas
The key is intentionality. Overbuilding is only a problem when it’s accidental.
A Better Mental Model
Think of Lovable as a powerful junior engineer. It will happily build everything unless you define limits. The quality of your MVP depends less on the tool and more on how clearly you define the goal.
An MVP isn’t a smaller product. It’s a learning instrument.
Conclusion
Lovable overbuilds MVPs because it’s optimized for usefulness, not restraint. Once you understand this, the fix is simple: constrain scope, define intent, and build deliberately.
Use Lovable to move fast—but don’t let speed turn into unnecessary complexity. The fastest MVP is the one that answers the question you actually care about, with the least amount of code possible.