3 Months of Building Documentation Tools: What I've Learned

My GitHub commit history tells a story: 3 AM commits, weekend marathons, and an unhealthy obsession with solving documentation.

After months of experiments, failed prototypes, and breakthrough moments, here's what I've learned about building truly helpful documentation tools.

Discovery #1: Context is Everything

My first prototype was naive. Feed code to AI, get docs out. Simple, right?

Wrong.

The AI would generate technically correct but completely useless documentation:

/**
 * Calculates the result
 * @param input - The input to calculate
 * @returns The calculated result
 */
function calculateResult(input) {
  // ... 200 lines of complex logic
}

Gee, thanks. Super helpful.

The breakthrough came when I realized: Good documentation needs to understand WHY, not just WHAT.

Discovery #2: Developers Hate Change

My second prototype required a new workflow. Custom comments, special syntax, a whole new way of thinking about docs.

Beta tester feedback was... brutal:

• "I'm not learning another syntax"
• "This better work with my existing setup"
• "If I have to change how I code, I'm out"

Fair enough.

The lesson? Documentation tools must fit into existing workflows, not create new ones.

Discovery #3: Perfect is the Enemy of Good

I spent two weeks building an AI that could generate "perfect" documentation. It analyzed code patterns, detected design patterns, even suggested improvements.

Users hated it.

Why? Because 80% accurate documentation that exists beats 100% perfect documentation that's still "coming soon."

Ship something helpful now, perfect it later.

The Technical Breakthroughs

Without giving away all the secret sauce, here are some technical wins:

1. Incremental Understanding

Instead of analyzing entire codebases at once (slow, expensive), CodeContext builds understanding incrementally. Each commit adds to its knowledge.

2. Pattern Learning

The AI learns YOUR patterns. Your naming conventions, your style, your way of structuring code. The docs it generates feel like you wrote them.

3. Smart Updates

Changed a function? CodeContext knows exactly which docs need updating. No more manual tracking.

The Failures (Yes, There Were Many)

The Over-Engineering Trap

I built a complex system for detecting "documentation decay." It had graphs, metrics, ML models...

Nobody cared. They just wanted docs that worked.

The Feature Creep Monster

Version 0.3 could generate docs in 5 formats, create diagrams, and make coffee (okay, not the last one).

Users wanted one thing: Clear, updated documentation. Period.

What's Actually Working

After all these experiments, here's what resonates with developers:

1. Zero Config Start - Run one command, get immediate value
2. Gradual Enhancement - Start simple, add features as needed
3. Escape Hatches - Always let developers override AI decisions
4. Transparent Process - Show what's happening, no magic black boxes

The Road Ahead

Building CodeContext has taught me that documentation isn't a technical problem - it's a human one.

The best AI in the world won't help if it doesn't understand how developers actually work. That's why every decision is filtered through one question:

"Will this actually help someone at 3 PM on a Friday when they're trying to understand legacy code?"

If the answer isn't a clear yes, it doesn't make the cut.

Let's Build This Together

I'm not building CodeContext in a vacuum. Every conversation with a frustrated developer shapes the product.

Have ideas? Horror stories? Feature requests? I want to hear them all.

Because the only way to fix documentation is to build something developers actually want to use.

Building in public at github.com/codecontext. Follow the journey and contribute your ideas.