From Overengineering to Clarity: What We Got Wrong Building Our Prompt Tool

Some projects fail quietly. Others force you to confront your mistakes head-on.

Our prompt tool was the second kind.

What started as an ambitious idea evolved into a complex system full of features, integrations, and analytics — but with less and less real value for the user.

This isn’t a showcase of what we built. It’s an honest breakdown of what we got wrong — and why it matters.

1. We Tried to Build for Everyone — and Built for No One

Our original vision was broad: a tool that works across multiple AI platforms, not just ChatGPT.

In theory, that sounded powerful. In practice, it was a nightmare.

Each platform came with:

• Different UI structures
• Different interaction patterns
• No stable APIs

The result?

Our extension kept breaking. A small UI change on any platform could break core features like saving prompts or opening modals.

👉 Lesson:

If you don’t control the platform, you don’t control your product.

2. We Built Features Before Validating Demand

We didn’t start small. We went all in.

We built:

• Categories and filtering systems
• Tags and types
• Workflows
• Version control with comparisons
• Analytics and benchmarking
• Usage charts and performance tracking

The problem wasn’t execution.

The problem was: no one needed this at the beginning.

👉 Lesson:

Complexity is not progress — it’s often just unvalidated effort.

3. We Followed “Cool Ideas” Instead of Real Behavior

Many features came from thoughts like:

• “This would be cool…”
• “This could be useful…”
• “We could analyze this…”

But we didn’t ask the most important question:

Would users actually use this every day?

Take prompt benchmarking with “Top Gainers” and “Top Losers” — inspired by stock markets.

Technically interesting. Visually impressive.

But practically? Rarely used.

👉 Lesson:

A feature is only valuable if it gets used consistently — not if it looks impressive.

4. We Tried to Solve Everything at Once

Our tool tried to be:

• A prompt manager
• An analytics platform
• A workflow engine
• A version control system
• An organization tool

It could do a lot.

But it didn’t do anything simply.

Users often just wanted one thing:

> “Save a prompt quickly and reuse it.”

Instead, they got:

• Filters
• Tags
• Categories
• Compatibility settings
• Multiple views

👉 Lesson:

Great products solve one problem extremely well — not ten problems poorly.

5. We Made UX More Complicated Than It Needed to Be

One of the most painful mistakes was user experience.

We introduced flows like:

• Popup → Main page → Modal → Next step
• Multi-step forms with “Next” and “Advanced”
• Separate views for similar actions

The result?

Too many clicks. Too much friction.

Saving a simple prompt became a process.

👉 Lesson:

Every extra click is a chance for users to leave.

6. We Scaled Too Early

We tried to think globally — too soon.

We added:

• 15+ languages
• Theme systems (dark mode, light mode)
• Complex UI structures

But none of this mattered without real usage.

👉 Lesson:

Scaling before product-market fit is wasted effort.

7. We Built Features That Weren’t Fully Functional

Some features sounded good but lacked real depth:

• Workflows with model selection (without real integration)
• New tab options without functionality
• Compatibility logic without technical foundation

This created a dangerous illusion:

The product felt more powerful than it actually was.

👉 Lesson:

Half-built features are worse than no features.

8. We Overcomplicated Styling

Instead of using proven frameworks like Bootstrap, we:

• Built custom CSS systems
• Styled layouts manually
• Created redundant code

The result:

• Hard to maintain
• Inconsistent design
• Slower development

👉 Lesson:

Standard tools exist for a reason — use them.

9. We Ignored a Simple Truth: Speed Matters Most

The biggest realization came late:

Users don’t want complexity.

They want speed.

In tools like ChatGPT, what matters is:

• How fast can I insert something?
• How fast can I save something?

Not:

• How detailed can I analyze it?

👉 Lesson:

Speed beats features. Every time.

The Real Problem: Lack of Focus

If we had to reduce everything to one core mistake, it’s this:

We didn’t have a clear focus on the problem we were solving.

We tried to build a perfect system —

instead of a simple, useful tool.

The Restart: A Simpler, Better Direction

Today, our direction is clear:

• Focus on one platform: ChatGPT
• Focus on a few essential features
• Focus on speed
• Focus on real usage

No overengineering.

No unnecessary abstractions.

No feature overload.

Final Thought

Mistakes like these aren’t failures.

They’re the turning point where better products begin.

Because once you understand what doesn’t work —

you can finally start building what does.

Try the Simpler Approach with PromptIn

If you’re tired of overcomplicated tools and just want a fast, reliable way to manage your prompts, this is exactly what we’re building now.

Give PromptIn a try and experience a cleaner, more focused workflow.

Install PromptIn for free and see how simple prompt management can be.