How one simple question can boost your project efficiency by 20-50% or send you spiraling back to square one

The Dangerous Question That Sounds Helpful

There’s one LLM prompt that can boost your project efficiency by 20-50% or destroy it so completely you’ll be rolling back to square one.

I’ve spent several days refactoring the same JavaScript code, watching it get worse with each “improvement.” You know that sinking feeling when your project goes backwards despite investing more time?

That was me, vibe coding a prompt generator frontend while learning JS on the fly, thinking LLMs would accelerate my learning journey.

Then I made the classic mistake.

I asked: “How can you improve it?”

Why This Question Is Like Giving Someone TNT to Solve Mining Problems

This question is like giving someone TNT to solve mining problems. Used properly, it can speed up discovering new gold mines. Used wrong? It blows up the whole tunnel and harms everyone around.

The problem isn’t the question itself—it’s how we typically use it without context, boundaries, or strategic thinking.

My Real-World Disaster: The Prompt Generator Project

The Setup

I was building a prompt generator frontend—a project that started clean and functional. The initial code structure was manageable, and I could navigate through the functions without getting lost.

The First Iteration: False Hope

With the first iteration, the structure of the code seemed to get better. It needed some manual intervention, but we were getting somewhere. The improvements looked promising, and I felt encouraged by the results.

The Trap Springs

Encouraged by these promising outcomes, I tried again. At first, it seemed like more improvements from the LLM. But when I looked deeper, I was shocked.

The LLM suggested removing the previous fixes!

By the 2nd-3rd iteration, I was already trapped in revision hell. My codebase became bigger, more vague, and less manageable—the opposite of what I wanted from refactoring.

The Cross-Model Reality Check

I tested this destructive pattern across Claude 3.5, Claude 3.7, Gemini, and OpenAI models:

• Claude models were more concrete despite the same underlying issues
• Gemini and OpenAI suggestions were even worse—more vague and less actionable

The problem wasn’t model-specific. It was methodology-specific.

The Hidden Cost of Improvement Loops

The Quality Degradation Pattern

Here’s what I discovered through painful experience: the first round of suggestions typically delivers the highest quality improvements. Each additional iteration increases the risk of “fixing the fixes” and making things worse.

Why Multiple Iterations Fail

• Context decay: The model loses track of the original goals
• Circular reasoning: AI starts fixing what it previously optimized
• Scope creep: Each iteration adds unnecessary complexity
• Decision fatigue: You lose the ability to distinguish good suggestions from bad ones

The Framework That Actually Works

After testing this approach across several different projects, here’s what consistently delivers results:

1. Define “Better” Before You Ask

Don’t ask for improvements without criteria. Instead of “How can you improve it?” try:

• “How can I make this code more maintainable?”
• “What would make this more performant?”
• “How can I reduce complexity while keeping functionality?”

2. Ask for Suggestions, Not Implementation

Replace “Improve this” with “What could be improved?” This subtle shift puts you in control of the decision-making process.

3. YOU Review, YOU Decide

Don’t let the AI drive the implementation bus. Review all suggestions first, evaluate them against your criteria, and consciously decide what makes sense for your specific context.

4. Implement Selectively

Only implement the changes that align with your defined goals. Resist the temptation to implement everything just because it sounds good.

5. Stop at One Iteration

This is the counterintuitive but crucial rule: do this only once. The first round of suggestions usually gives you the biggest improvement with the smallest risk.

The Business Impact of Getting This Right

Before: The Chaos

• Days spent in revision loops
• Decreasing code quality over time
• Frustration with AI collaboration
• Learning progress stalled by confusion

After: Controlled Collaboration

• Efficient improvement cycles
• Predictable quality outcomes
• AI feels like a collaborative partner
• Accelerated learning and development

For enterprise environments, this methodology difference can mean:

• Reduced development time by 20-50%
• Improved code maintainability
• Better team adoption of AI tools
• Lower technical debt accumulation

Implementation Guide for Teams

For Individual Developers

1. Set improvement criteria before engaging with AI
2. Batch your improvement requests rather than iterating continuously
3. Document what works to build your personal AI collaboration playbook
4. Share learnings with your team to prevent others from falling into the same traps

For Team Leaders

1. Establish AI collaboration guidelines based on these principles
2. Train team members on structured AI interaction
3. Create review checkpoints for AI-assisted development
4. Monitor for improvement loop patterns in code reviews

For Organizations

1. Develop AI collaboration standards across development teams
2. Include AI interaction training in onboarding processes
3. Establish metrics for measuring AI collaboration effectiveness
4. Create knowledge sharing mechanisms for AI best practices

The Meta-Lesson: Process Over Prompts

The magic isn’t in the prompt—it’s in the process around it.

This experience taught me that successful AI collaboration requires the same strategic thinking we apply to human collaboration:

• Clear communication of goals and constraints
• Structured interaction patterns
• Quality control mechanisms
• Iterative improvement with defined stopping points

Your Next Steps

Ready to transform your AI collaboration from chaos to control?

Immediate Actions

1. Audit your current AI workflows for improvement loop patterns
2. Define quality criteria for your typical AI-assisted tasks
3. Test the one-iteration rule on your next project
4. Document what works for future reference

Longer-term Development

1. Share these insights with your team or organization
2. Develop team-specific guidelines based on your domain expertise
3. Create training materials for effective AI collaboration
4. Build measurement systems to track improvement in AI-assisted outcomes

The difference between effective and destructive AI collaboration often comes down to methodology, not technology. Master the process, and the prompts will follow.

Want more practical AI transformation insights from someone implementing it at enterprise scale? Connect with me for real-world lessons learned from the trenches of digital transformation.