26. October 2025
From Chaos To Structure: My AI Development Journey
Picture this: It’s 7 AM, you’re on your first cup of coffee, and you’ve just discovered that your AI model can quickly help you explore an idea to establish whether it’s worth exploring further. Do you stop to write a formal specification, or do you ride the wave of discovery?
Over the past year, I’ve lived in both worlds—the exhilarating chaos of AI experimentation and the structured discipline of traditional software design. What I’ve learned is that the future of AI development isn’t about choosing one approach over the other; it’s about knowing when to embrace the chaos and when to impose order.
This is the story of how I discovered the power of Vibe Coding and learned to balance it with Spec-Based Design to build AI systems that actually work.
The Projects Thus Far
Before diving into methodology, let me show you what this journey has produced. Each project taught me something different about when to follow my instincts versus when to plan ahead:
- Kanban Board - Pure vibe coding. Started with “wouldn’t it be cool if…” and ended up with a working board in hours.
- IDEO Design Sprint Game - Hybrid approach. Vibe-coded the core mechanics, then spec’d out the game flow.
- Working Backwards PRD Generator - Started as exploration, became a structured tool for product planning.
Each project pushed me to question: When should you trust your gut, and when should you trust your process?
The Tale of Two Approaches
Vibe Coding: Following the Breadcrumbs
Imagine you’re an explorer in uncharted territory. You don’t have a map because the landscape changes daily. This is vibe coding—development driven by intuition, experimentation, and the willingness to follow interesting breadcrumbs wherever they lead.
Vibe coding thrives when you:
- Have a feeling about what might work, but no clear path to get there
- Need to move fast because the AI landscape shifts weekly
- Want to discover what’s actually possible, not just what you think is possible
- Can afford to throw away code in pursuit of breakthrough insights
It’s messy, unpredictable, and absolutely essential for AI innovation.
Spec-Based Design: Building the Cathedral
Now imagine you’re an architect. You have blueprints, building codes, and a clear vision of the final structure. This is spec-based design—methodical, documented, and built to last.
Spec-based design excels when you:
- Know exactly what you’re building and why
- Need multiple people to work together seamlessly
- Can’t afford to rebuild from scratch every few months
- Require reliability and maintainability over rapid discovery
It’s structured, predictable, and absolutely essential for AI systems that matter.
The Evolution of My Process
The Vibe Phase: Where Magic Happens
I start most AI projects in what I call “vibe mode” because AI development has a unique characteristic: you often don’t know what’s possible until you try it.
The Reality Check: Traditional software has predictable APIs and well-documented behaviors. AI models? They surprise you. GPT-4 might excel at something Claude struggles with, or a simple prompt tweak might unlock capabilities you never expected.
Speed Over Perfection: In a field where new models drop monthly and capabilities evolve weekly, formal specifications can become obsolete before you finish writing them. Vibe coding lets you ride the wave of innovation instead of being crushed by it.
Discovery-Driven Development: The best AI features often emerge from happy accidents. That weird edge case in your prompt might become your app’s killer feature.
The Transition: When Chaos Needs Structure
But here’s what I learned the hard way: vibe coding has an expiration date. There’s a moment in every project where continued exploration becomes procrastination, and creative freedom becomes technical debt.
The transition happens when:
- Your prototype actually works and people want to use it
- You need to explain your system to someone else
- Performance and reliability start mattering more than features
- You catch yourself rebuilding the same components repeatedly
This is when I shift gears.
The Hard-Won Lessons
When Vibe Coding Shines
Through trial and error (mostly error), I’ve learned that vibe coding is your secret weapon for:
- Model Exploration: Testing what GPT-4, Claude, or the latest model can actually do versus what the docs claim
- Data Pipeline Discovery: Understanding your data’s quirks before you architect around assumptions
- User Experience Breakthroughs: Finding the interaction patterns that feel magical, not just functional
- Integration Reality Checks: Discovering API limitations and unexpected behaviors before you commit to an architecture
When Structure Saves Your Sanity
But spec-based design becomes non-negotiable for:
- Production Systems: When uptime matters more than experimentation
- Team Collaboration: When your brilliant vibe-coded solution needs to make sense to others
- Complex AI Orchestration: When multiple models need to work together reliably
- Performance Optimization: When you need to squeeze every millisecond out of your inference pipeline
The trick isn’t choosing one over the other—it’s knowing when to switch gears.
Best Practices I’ve Developed
- Time-box vibe coding phases - Set clear limits to prevent endless exploration
- Document discoveries - Capture key learnings from experimental phases
- Version control experiments - Even throwaway code can provide valuable insights later
- Plan the transition - Know when and how to move from exploration to formal development
- Embrace hybrid approaches - Some components benefit from vibe coding even in formal projects
The AI Development Reality
AI development is inherently experimental. Models behave unpredictably, APIs change frequently, and the best practices are still emerging. This reality makes vibe coding not just useful, but often necessary for successful AI projects.
The key is knowing when to embrace the chaos of exploration and when to impose the structure of formal design.
My AI Project Development Flow
Through my experience, I’ve developed a structured approach that combines the best of both vibe coding and spec-based design:
1. Brainstorming Phase
I start with the IDEO “How might we…” approach for initial ideation. This opens up creative possibilities and frames problems as opportunities.
2. Requirements Definition
Focus on outlining key requirements by answering:
- Why: What problem are we solving?
- How: What approach will we take?
- What: What technologies are required?
3. Prototype Development
Work collaboratively with LLMs (like Claude/Chatgpt) to create proof-of-concepts that test the core idea. I track LLM versions for reproducibility.
4. Iterate & Refine
Test and evaluate the prototype, suggesting improvements and refinements based on results.
5. Feedback Loops
If the prototype doesn’t work as expected, I cycle back to earlier steps - whether that’s revisiting requirements or even returning to brainstorming.
6. MVP Transition
Once the idea feels good enough to be considered an MVP (Minimum Viable Product), I proceed to spec-based development. This transition point is crucial - it’s where exploration becomes execution.
The Spec-Based Advantage for Growth
The real power of transitioning to spec-based design becomes apparent as projects grow. Unlike pure vibe-based approaches, spec-based development allows me to:
- Add new requirements systematically without breaking existing functionality
- Scale the project with confidence in the underlying architecture
- Collaborate effectively with team members through clear documentation
- Maintain code quality as complexity increases
This hybrid approach leverages the creative discovery of vibe coding while providing the structural foundation needed for sustainable growth.
What’s Next
I’m continuing to refine this hybrid approach as I work on new AI projects. The balance between exploration and structure seems to be the sweet spot for productive AI development.
Have you experimented with different development approaches for AI projects? I’d love to hear about your experiences.
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