The Insane Reality of AI-Powered Development

This is insane.

I’m working on what seems like a small project, but as I pair with AI, I’m discovering how many places I would have hit problems, questions, and blockers in previous projects.

The difference?

All of these potential roadblocks are being solved proactively by using AI with comprehensive project context and living documentation.

Let me explain how I got here.

The Setup: More Than Just Code Completion

I’ve been using Cursor (which runs on Claude-3.5-sonnet) to revolutionize not just coding, but the entire development process, documentation maintenance, and project planning.

Here’s what happened when I started breaking down the project with AI:

1. I gave it my initial product doc and some API/environment documentation
2. It built a technical blueprint, breaking down the idea into focus areas and surfacing critical questions
3. I used ChatGPT to research those questions, gathering answers with documentation links
4. Fed those answers back to Claude to update and reorganize the blueprint

What would have taken days of research and documentation took hours. But that was just the beginning.

Documentation Evolution: From Messy to Maintainable

I decided to take this to the next level:

1. Added JSDoc to every file and implemented ESLint to enforce documentation standards
2. Had the AI clean everything up — a task that would have taken days manually
3. Implemented a progressive disclosure approach with index.md files in each directory
4. Each index summarizes its directory and all contained files
5. Creates a natural documentation hierarchy
6. Makes everything discoverable and maintainable

The magic here isn’t just that the documentation exists — it’s that it stays current. Every code change, every decision, every new feature automatically triggers documentation updates. The AI handles this as the final step of any task, keeping everything in sync.

Process Transformation: Capturing the “How” and “Why”

Here’s where it gets really interesting. I had the AI analyze our entire development discussion to:

• Track HOW we’re building this project
• Document decisions as they’re made
• Capture when decisions happened
• Record the ripple effects of each choice

For example, when we decided to implement progressive documentation structure, the AI:

• Documented the decision with full context
• Updated all affected documentation
• Created new index files where needed
• Maintained cross-references automatically

The Real Impact

The time savings are dramatic — tasks that would take days now take hours. But it’s more than that:

1. Better Decision Making Every choice is documented with context Future decisions are informed by past ones No more “why did we do it this way?”
2. Proactive Problem Solving AI spots potential issues before they become problems Documentation gaps are identified and filled Dependencies are tracked and managed
3. Reduced Cognitive Load Don’t have to remember every decision Documentation is always where you expect it Easy to pick up where you left off

Making This Repeatable

The best part? This isn’t a one-off success. I’ve created a template for future projects that includes:

• Documentation structure and standards
• Decision tracking processes
• AI integration patterns
• Automated maintenance workflows

What started as an experiment has become a complete transformation of how I am going to approach projects going forward. The AI isn’t just helping write code — it’s an active partner in planning, documentation, and decision-making.

And yes, I wrote the original version of this post and then the AI helped me level it up.